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The “Data Rich, Insight Poor” Paradox in Modern Enterprises

Most organizations have spent the last decade focused on data ingestion. They have successfully checked the boxes for cloud migration, CRM implementation, and IoT connectivity. However, the accumulation of data has outpaced the organizational ability to interpret it. This paradox creates a “blind spot” where leadership assumes they are data-driven because they have reports, while in reality, those reports are lagging indicators that offer no predictive power.

The Hidden Costs of Data Silos and Fragmented Architecture

Data silos are not just a technical nuisance; they are a financial drain. When a public institution or a large enterprise operates on fragmented architecture, it creates a “shadow IT” environment where different departments rely on conflicting datasets.

• Operational Friction: teams waste hours in cross-departmental meetings trying to reconcile different versions of the same metric;
• Inconsistent Customer Experience: without a unified data view, a customer might receive a marketing offer for a product they just complained about to support;
• Resource Misallocation: IT teams spend their time building manual “bridges” between systems rather than innovating on core business products.

Why More Data Doesn’t Always Lead to Better Business Agility

There is a common misconception that “more data equals more certainty.” In the fast-paced USA market, the opposite is often true. High volumes of unrefined data create “noise” that masks critical market signals.

True business agility is the ability to pivot based on data-derived triggers. If your data architecture doesn’t allow you to identify a supply chain bottleneck or a shift in consumer sentiment until the monthly review, the data has failed its primary purpose. Agility requires a shift from “Total Data Collection” to “High-Signal Intelligence,” where the infrastructure is tuned to filter out the noise and highlight the variables that actually impact the bottom line.

Bridging the Divide with Strategic Data Strategy Consulting Services

Bridging the execution gap requires more than just a new software license; it requires a blueprint that connects IT capabilities to executive objectives. Professional data strategy consulting services serve as the architect of this bridge, ensuring that the technology stack is not just operational, but “decision-ready.”

Moving forward, we address the strategic shift required to solve these structural issues. This is where the technical architecture meets business intent.

Aligning Technical Infrastructure with Executive KPIs

Many digital initiatives fail because the technical teams are optimizing for “uptime” and “storage,” while the C-suite is optimizing for “revenue” and “market share.” A strategic consultant translates these business goals into technical requirements.

• Reverse-Engineering the Stack: instead of asking “What data can we collect?”, we ask “What decision-making process is broken?” and build the data pipeline to fix it;
• Metric Standardization: establishing a unified set of KPIs ensures that when the CEO looks at a dashboard, the data aligns with the CFO’s financial reports and the COO’s operational reality.

Building a Roadmap for Scalable and Cost-Effective Data Management

For public institutions and corporations, “cost-effectiveness” isn’t just about the initial bill—it’s about the total cost of ownership (TCO). A strategic roadmap prevents the “cloud sprawl” that occurs when organizations store petabytes of data they never use. By implementing a tiered data strategy, businesses can keep high-value, frequently accessed data in high-performance environments while moving archival data to lower-cost storage, significantly optimizing the IT budget.

Common Pitfalls: Why Collecting Data is Only Half the Battle

Even with a strong roadmap, the human and procedural elements often become the undoing of digital transformation. If the organization treats data as a passive asset rather than an active driver of culture, the transformation will stall.

Underestimating the Importance of Data Governance and Quality

Data without governance is a liability. In the USA, where data privacy regulations and security standards are increasingly stringent, governance is no longer optional.

• The Trust Layer: if managers don’t trust the data, they will continue to rely on “gut feeling”. Governance ensures accuracy, lineage, and security, creating the trust needed for widespread adoption;
• Data Stewardship: successful enterprises assign ownership to data. When someone is responsible for the quality of a specific dataset, the “garbage in, garbage out” cycle is broken.

The Failure to Integrate Analytics into Daily Workflows

The most sophisticated analytics platform in the world is useless if it exists in a vacuum. Transformation fails when insights are delivered via a separate portal that employees have to remember to log into.

• Operational Integration: real value is created when insights are pushed directly into the tools your team already uses—be it a CRM, an ERP system, or even internal communication channels like Slack or Teams;
• Actionable Dashboards: a dashboard should do more than show a graph; it should suggest a next step. Modernization means moving from “What happened?” to “What should we do next?”.

How to Modernize Your Decision-Making Process

Modernization is the transition from being a reactive organization to a predictive one. This requires a fundamental shift in how resources—both technical and human—are deployed.

Most companies use data to explain why they missed a target last quarter. Proactive intelligence uses that same data to predict where the market is moving.

• Predictive Modeling: by analyzing historical patterns, enterprises can forecast demand, anticipate equipment failure, or identify at-risk customers before they churn;
• Scenario Simulation: modern data strategies allow leaders to run “What if” scenarios, giving them the flexibility to test strategies in a digital sandbox before committing real-world resources.

The Role of External Expertise in Successful Transformation

Internal teams are often tasked with “keeping the lights on,” leaving little room for the radical rethinking required for a true transformation. External specialists bring a perspective forged across various industries and technical environments.

Reducing Time-to-Value with Specialized IT Consulting

Experience allows consultants to identify and bypass the architectural bottlenecks that typically stall projects for months. This specialized knowledge accelerates the journey from the “planning phase” to the “value-delivery phase,” ensuring that the transformation begins paying for itself sooner.

Navigating Complex Digital Ecosystems with Proven Frameworks

Whether integrating legacy systems with modern cloud environments or deploying sophisticated data warehouses, professional consultants utilize proven frameworks. They don’t just build a siloed solution; they build an ecosystem designed for interoperability and long-term resilience.

Turning Data into Your Competitive Advantage

Digital transformation is not a one-time upgrade; it is a fundamental shift in operational philosophy. The gap between data collection and decision-making can only be closed when technology is viewed as a servant to strategy. For organizations seeking to move to the next level of maturity, the priority is clear: move beyond the collection phase and start engineering a decision-driven enterprise.

The post Why Digital Transformations Fail – The Gap Between Data Collection and Decision Making appeared first on The European Business Review.

By Satish Thiagarajan

Intuition used to be something of a be-all for CEOs. But with transparency and accountability now prominent business priorities, gut instinct is no longer enough. In this piece, Brysa’s CEO, Satish Thiagarajan, discusses how data-driven tools, such as CRM systems, analytics platforms, and predictive forecasting, can empower leaders to make better decisions quickly. Intuition isn’t dead, but it does need to be substantiated.

The role of the CEO isn’t what it once was. The gut and bravado that once made careers isn’t enough to ensure success in a period characterised by disruption, volatility, and stakeholder scrutiny.  Intuition helps, but you can’t rely on it alone to see you through the everyday complexities associated with running a business. Not when one mistake can have lasting consequences for everyone connected to the company. That’s why data is playing an increasingly important role in the lives of all CEOs. Bringing unexpected insight and much-needed clarity, it can support CEOs in a way that instinct has never been able to. You just need to know how to use it properly.

Why data can be worth more than intuition

You don’t have to dig deep into business history to find cautionary tales of leaders who paid the price for relying too heavily on gut feeling. From Kodak to Nokia, some of the world’s most iconic companies have stuck to what they knew, placing instinct over insight and dismissing emerging data trends, only to lose millions in market value and be confronted by a steep decline in relevance. Showing clearly that no one is immune to disruption. The take home here, however, isn’t that there intuition was wrong or even completely lacking, the problem came from the decision to ignore the data. Intuition can carry enormous value for a business, but it has to be supported by something firmer. When that doesn’t happen, small risks can quickly multiply. For Kodak, digital disruption led directly to capital misallocation and slower responses to market shifts. For others, it’s resulted in regulatory mishaps, compliance breaches, and even the erosion of talent and company culture. All things that the strategic use of data could have helped to prevent.

Data doesn’t just validate your intuition, it opens your perspective, and allows you to make your strategies defensible. The idea is to ground every vision and every decision in evidence. But you need tools to do that.

What tools can help CEOs access the data they need?

There’s a whole host of business tech on the market, and it’s changing so fast that much of what you see today will have been replaced by the same time next year. So, how do you know which tools to invest in for longer-term value?

CRM systems

Customer Relationship Management (CRM) systems used to be little more than sales tools, but they are now providing CEOs with the data they need to make better decisions. So, whether you choose Salesforce, Monday.com, Microsoft Dynamics, the core objective is to amalgamate customer data across every touchpoint, monitor interactions across departments, and deliver real-time visibility into pipeline performance, customer satisfaction, and revenue trends. Add in integrated analytics and customisable dashboards, and you can transform raw data into actionable insights that directly support growth, innovation, and long-term customer loyalty, basically helping your business to stay ahead.

Reporting and analytics tools

Raw data is notoriously difficult to interpret, which is why you need reporting and analytics tools. Platforms like Tableau, Power BI, and Looker transform large volumes of unstructured data into meaningful, visual insights that are easy to understand and act on. These tools allow CEOs and leadership teams to track KPIs in real time, identify trends, and put their focus where it’s needed most. By cutting through the noise, analytics tools provide a clear picture of a business’ performance, so it’s easier to measure success, spot risks early, and make confident, data-driven decisions.

Predictive analytics and forecasting tools

While it’s impossible to predict the future with absolute certainty, predictive analytics and forecasting tools do a pretty good job. With solutions like Salesforce Einstein Analytics, SAP Analytics Cloud, and IBM Watson, historical and real-time data is used to identify patterns, anticipate market shifts, and model future scenarios. And it’s surprisingly accurate. Helping you to spot risks before they happen, plan stock and staff allocation, and act on opportunities before your competition.

Collaboration tools

This one isn’t so much about decision-making, but rather ensuring that any decisions you do make are effectively communicated, understood, and implemented across the business. Using platforms like Slack, Microsoft Teams, and Salesforce Chatter can help both leadership and the wider staff to share knowledge and enhance both transparency and accountability.

A dashboard tool

Your dashboard tool is your way to bring everything else together. Because, what’s the point in having all of these tools if you don’t monitor and use them properly? A good dashboard tool, like Microsoft Power BI, Tableau, and Google Looker Studio, will provide you with a real-time, visual overview of the metrics that matter most to your business. It doesn’t matter what you’re tracking, if it’s important, it should be available through your dashboard, delivering what I like to call a single source of truth for your business.

Don’t get me wrong, I’m not saying that intuition doesn’t matter. Of course it does. But it’s no longer enough on its own. Businesses and customers now demand transparency and accountability above all else, and your gut instinct simply can’t deliver that. But when you reinforce it with data, you can speak with the confidence of irrefutable facts at your fingertips.

About the Author

Satish Thiagarajan is the founder of Brysa, a Salesforce and data consultancy based in the UK. His company advises media, industrial, and services clients on using Data Cloud and Agentforce to turn signals into action. His work focuses on closing the loop between insight and execution in sales, marketing, and service.

The post Beyond Gut Feelings: What Today’s CEOs Need to Survive and Succeed appeared first on The European Business Review.

Modern enterprises are drowning in data, yet few have mastered the art of transforming data into trusted, actionable value. Every day, leaders face hurdles such as business units entrenched in their own silos, dashboards that provoke skepticism rather than confidence, and analytics platforms that struggle to scale as organizations grow. These issues aren’t just technical roadblocks, but strategic threats that hinder competitive advantage, stall digital transformation, and constrain visionary thinking.

Data engineering, a discipline once relegated to the IT back office, is now central to boardroom discussions. The answer lies in its ability to orchestrate data into a strategic asset, not simply a system output. Boards and CXOs are pressed to understand how data can underpin trust, agility, and opportunity, and, critically, how missteps can erode those foundations. Data engineering is no longer an option; it is the engine behind scalable enterprise intelligence.

What Is Data Engineering Consulting?

It has evolved dramatically, from scripting ETL processes for static warehouses to architecting adaptive, domain-driven “data climates” where lakehouses, data meshes, and automation tools drive resilience and flexibility. In fact, there are various data engineering tools and frameworks for building scalable data pipelines. However, let’s explore some of the key components of data engineering consulting in detail: 

• Strategy: Partners collaborate to define the data vision, mapping strategic objectives to the right data assets and use cases. This step sets the tone for transformation, wherein CXOs need to ask, “What outcomes matter?”
• Architecture: From cloud-native lakes to hybrid mesh structures, today’s architecture must anticipate exponential data growth, security mandates, and real-time use cases. It is less about technology choice, more about design for adaptability and sustainability.
• Tooling and Pipelines: Modern solutions span ingestion, transformation, quality control, and monitoring, all driven by automation and observability. The objective is not only reliability but proactive scaling.
• Governance: Trust is built or broken here. Governance applies policies, lineage controls, and stewardship to ensure that data is not just accessible but credible and compliant.

Deliverables

• Unified blueprints for scalable data architecture.
• Automated, observable data pipelines.
• Embedded governance and compliance frameworks.
• ROI-driven technology modernization plans
• KPIs that connect operational improvements to business impact.

Engagements now encourage data climate thinking, moving enterprises beyond static warehouses to structures that evolve with business ambition. Data meshes decentralize ownership, while lakehouses harmonize analytics and reliability, and automation ensures resilience in dynamic markets.

Forward-thinking CXOs know that the real value of consulting lies in bridging strategy and execution, the “why” behind every innovation program.

Why Should Businesses Invest in Data Engineering Consulting?

Data engineering consulting is not just a technical upgrade. When data initiatives fail, it is often because strategy and execution drift apart. Consulting bridges that divide, ensuring every investment in analytics yields measurable results.

1. Speed and Agility

Enterprises must outpace their competitors. High-performance data infrastructure collapses the time between insight and action, fueling real-time decisions and adaptive strategies. Consider how digital-native retailers pivot instantly in response to shifting demand, or how banks use streaming data to manage instant fraud detection. Consulting teams enable these capabilities by designing pipelines that adapt and scale at a moment’s notice.

2. Scalability and Resilience

Data platforms are the arteries of digital businesses; if they choke, growth stalls. Consulting arms organizations with elastic architectures that are cloud-native, distributed, and self-healing in nature, turning capacity constraints into competitive strengths. Logistics giants and manufacturers now process billions of events seamlessly, optimizing supply chains and delivery networks when the stakes are highest.

3. Compliance and Trust

Trust is intelligent risk-taking. Regulatory disruption is constant—GDPR, CCPA, PCI DSS, and emerging AI governance models. Data consulting builds compliance into the DNA of the organization, embedding lineage tracking, audit trails, and access controls from source to dashboard. For CXOs, peace of mind in the audit room is linked directly to consulting engagement quality.

4. Cost Optimization

Data engineering services drive enduring efficiency by rationalizing toolkits, eliminating redundancy, and automating manual pain points. The result? Tangible cost reduction, faster analytics, and best-in-class ROI on cloud and infrastructure investment. A leading investment bank saved 35% annually on analytics operations after consultants streamlined its data estate. This proves that savings and speed can coexist.

The next vital step for CXOs is to structure these engagements for long-term business impact—not just technical completeness.

How Can CXOs Engineer Successful Consulting Engagements?

Success hinges on design, not improvisation. The right engagement model, delivered by an ecosystem-aware partner, becomes the core of enterprise transformation.

What Engagement Models Create Value?

• Strategic Assessment: Short, targeted diagnostics revealing gaps, risks, and opportunities. Ideal for companies at the start of their journey or facing regulatory urgency.
• Implementation Partnership: Core co-creation model, such as design, build, and operationalize data platforms while transferring know-how.
• Managed Services: Ongoing, proactive maintenance and optimization, keeping data operations aligned with business evolution.

Selection depends on digital maturity, ambition, and internal talent. CXOs should target the best fit for organizational momentum.

What Makes a Partner Future-Proof?

• Tech Agnosticism: Champions solutions tailored to business goals, not vendor incentives; evidence of multi-cloud capability is vital.
• Domain Expertise: Connects data strategy with sector context, as telco needs differ from fintech or pharma.
• Operational Maturity: Demonstrated governance, security, and transparent delivery—evidenced through outcome-based reporting.

How to Align Teams for Lasting Impact?

Vision must travel across hierarchies. Successful consulting breaks silos with workshops, co-design sessions, and executive scorecards. Alignment unlocks the power of data as a shared asset when CFOs and heads of business debate metrics, dashboards evolve into trusted tools.

Which Metrics Measure Success?

Define and track success with clarity:

• Insight turnaround (hours, not days).
• Automation rates across pipelines.
• Data trust index, verified through external audits.
• Cost efficiency per analytical query.
• Business impact KPIs, such as market share, customer retention, or accelerated product launches.

Once CXOs have structured the partnership, timing becomes the strategic question.

When Is the Right Time to Engage Data Engineering Consultants?

As digital urgency accelerates, timing is everything. Consulting is most effective when it anticipates disruption, not merely responds to crisis.

Key Triggers Signaling Readiness

• Data Chaos: Redundant, conflicting pipelines undermine business logic and expose competitive gaps.
• Inconsistent Metrics: Multiple versions of the “truth” stall executive decision-making.
• Stalled Initiatives: Persistent project delays, escalating costs, or an inability to scale signal the need for fresh architecture.
• M&A Activity: Unifying disparate cultures, systems, and datasets calls for expert blueprinting.
• Regulatory Pressure: New privacy laws and security mandates necessitate rapid governance deployment.
• AI/ML Ambition: Evolving from static reporting to predictive and generative analytics demands higher fidelity in core data flows.

Consulting transforms these problems into new possibility frontiers, and CXOs who sense inflection points will act ahead of market needs.

To stay relevant tomorrow, CXOs must anticipate future trends and embed them into today’s strategy.

Where Is Data Engineering Consulting Heading?

The frontier for data engineering services is not incremental improvement; it is transformation itself. As the landscape matures, several paradigms will redefine enterprise value.

1. AI-Powered DataOps

AI moves from hype to foundation, automating pipeline orchestration, predicting failures, and recommending optimizations. Data engineers spend less time troubleshooting and more time innovating. Next-gen consultants design AI-driven, self-healing architectures that detect anomalies and adapt in real time.

2. Real-Time Analytics Ecosystems

Static insights yield to instantaneous intelligence. Enterprises are shifting to continuous event-driven analytics, leveraging technologies like Apache Kafka, Spark Streaming, and cloud-native services for instant risk management, customer engagement, and operations optimization. For CXOs, competitive advantage will be measured in milliseconds.

3. Data Contracts

In decentralized organizations, data contracts formalize schema, SLAs, and stewardship between teams. They ensure reliability and accountability, enabling business units to move fast without compromising system integrity. Consultants structure these agreements to balance autonomy and control.

4. Compliance Automation

Manual audits and governance checks will soon be obsolete. Consultants embed policy engines and AI monitors that automate classification, tagging, access management, and alerting for sensitive data across geographies. Future CXOs will audit compliance in real time. 

Conclusion

Data engineering consulting is reshaping the enterprise landscape, fueling innovation, enabling resilience, and rekindling executive ambition in the age of intelligent data. For forward-thinking CXOs, the imperative is clear: engineer value, don’t just inherit it. Architect trust into every system, future-proof business operations, and foster a culture where data is the raw material of competitive advantage.

The future belongs to those willing to reimagine how enterprise intelligence is built, trusted, and optimized. Engaging with a visionary data engineering services company is more than a modern necessity; it is the defining difference between legacy limitation and lasting leadership.

The post A CXO’s Guide for Data Engineering Consulting: What, Why, How, and When appeared first on The European Business Review.

By Markus Noga

As the digital landscape evolves amid shifting global politics, small- and medium-sized businesses (SMBs) must take decisive action to safeguard their data. This article explores the growing importance of digital sovereignty and how adopting GDPR-compliant, European-based cloud solutions can shield SMBs from regulatory risk while fostering trust and resilience.

Mounting global tensions and sweeping regulatory changes have placed European SMBs at the forefront of a critical challenge: digital sovereignty. Once a forward-looking concept, it has now become a non-negotiable reality. Yet many remain unaware of the urgent need to act. Despite increasing concerns over foreign access to data and the dominance of non-European cloud providers, a large proportion of SMBs across the region have yet to prioritise the security of their digital independence. 

Digital sovereignty refers to the ability of businesses to control and protect their data and digital infrastructure within the bounds of local laws and regulatory frameworks. For European SMBs, this concept has never been more vital, particularly as major US cloud providers dominate the market. A concerning practice, “sovereignty washing,” has taken root, whereby providers position themselves as compliant with local regulations while still being subject to foreign laws, such as the US CLOUD Act. This situation exposes European SMBs to risks associated with foreign government access to data. 

Data control across Europe 

A recent IONOS study, conducted with YouGov, polled decision-makers across multiple European markets and revealed that many small- and medium-sized businesses across Europe view IT security and data protection as a key area of focus in their companies’ digitalisation efforts. IT security and data protection ranked as a priority for 49% of UK businesses, 46% of German businesses and 53% of French businesses, second only to improving the visibility of their companies on the internet. Despite this emphasis, substantial barriers still hinder progress. Limited time (46%) and high costs (54%) remain the most prominent challenges faced by businesses over the past two years.  

The need for sovereign solutions 

Geopolitical tensions and evolving global legislation further heighten the urgency for European SMBs to act not just on data security, but on digital sovereignty. Legislation such as the US CLOUD Act amplifies concerns about foreign access to sensitive data, creating significant challenges for organisations relying on non-European cloud providers. Cloud services operated entirely within the EU offer not just GDPR compliance, but also protection from extraterritorial laws that could compromise data privacy. These regional safeguards are becoming a crucial criterion for businesses re-evaluating their cloud infrastructure. According to IONOS’ study, 83% of SMBs expect technology providers to proactively protect their information from regulatory risks and foreign interference. These challenges underscore the necessity of adopting GDPR-compliant, European-based cloud solutions to enhance security and reduce exposure to external threats. 

European sovereign cloud solutions offer a critical resource for SMBs, providing robust cloud services that secure data and comply with local privacy laws. Not only do these safeguards mitigate risks stemming from foreign interference, but they also ensure businesses are better equipped to navigate uncertain regulatory landscapes in the future. Protecting valuable business data amidst geopolitical unpredictability is essential for securing long-term success and operational security. 

Simplifying the path to digital sovereignty 

Although achieving digital sovereignty may seem complex, there are clear, actionable steps SMBs across Europe can take to simplify the process. For many SMBs, the road to sovereignty must be both secure and manageable. Cloud providers with strong local expertise can help businesses implement compliance-focused infrastructure without excessive complexity or cost. 

True sovereignty begins with ensuring that the ultimate parent company of the provider is headquartered in Europe, as this guarantees that the provider operates exclusively under European laws and is shielded from foreign interference. Equally important is that data centres are located within European jurisdictions, ensuring compliance with GDPR and protecting sensitive information from extraterritorial laws such as the US CLOUD Act. Furthermore, providers should employ staff based in Europe, enabling businesses to benefit from local expertise and ensuring that data management aligns with regional standards and practices. Finally, the technology must be managed autonomously, avoiding dependencies on external entities that could compromise data security and sovereigntyBy engaging with providers that have a strong European presence and can demonstrate compliance with local security standards, businesses can reduce exposure to foreign interference and safeguard their data. 

Furthermore, integrating European-based cloud systems with open-source platforms empowers SMBs to maintain flexibility and control over their data infrastructure. Open-source platforms minimise dependency on single vendors, enabling businesses to adjust their digital strategies in response to shifting legal or technological developments. For European businesses, this combination of European-based systems and open-source tools offers a balanced approach to ensuring data security without compromising innovation. Guidance from providers that combine secure infrastructure with expert consultation can help businesses navigate the regulatory landscape with greater confidence. 

Sovereignty in business strategy 

Making IT security and data protection central to business strategy is a critical measure for European SMBs aiming to achieve digital sovereignty. This involves implementing best practices for secure data handling, conducting regular risk assessments, and fostering a culture of compliance within organisations. By embedding these principles into their operations, businesses can better align with the evolving digital landscape while protecting themselves against future disruptions. 

Digital sovereignty represents far more than a regulatory requirement. It signals a strong commitment to data privacy and security, values that resonate deeply with stakeholders. SMBs that prioritise sovereignty not only protect their operations but also build trust with their customers and partners, differentiating themselves in a competitive market. 

As the global economy grows increasingly interconnected, trust and transparency in data management are becoming determining factors for business success. Customers, partners, and regulators alike are placing higher expectations on organisations to demonstrate strong data ethics. SMBs that address these expectations can enhance their reputation and future-proof their operations against emerging challenges. 

The digital frontier is expanding rapidly and European SMBs face a critical choice. Those that act decisively and adopt GDPR-compliant, European-based cloud solutions will not only secure their operations but also position themselves as resilient and trustworthy leaders in their industries. Digital sovereignty is no longer just an IT consideration, it is a strategic imperative. By safeguarding their data and aligning practices with local regulations, European SMBs can navigate an uncertain world with confidence, ensuring long-term success and operational security. 

About the Author

As CTO of IONOS, Markus Noga brings a compelling combination of technical expertise and strategic business insight. Previously serving in senior AI and Cloud leadership positions at SAP, he offers a complex understanding of both the technical and geopolitical dimensions of cloud computing.  

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The engineering and construction sector faces a striking paradox: digital tools are proven to boost performance, yet adoption remains low. In this article, Pedro Hidalgo Insua explores how forward-thinking EPCs can turn this gap into an advantage, using AI, integrated data ecosystems, and modern commissioning tools to outperform slow-digitalizing competitors and strengthen long-term project success.

A report published at the end of 2024 by the Royal Institution of Chartered Surveyors highlights a paradox in which the engineering and construction industry finds itself – globally, particularly in Europe.

Here’s the paradox: most professionals agree that digital tools improve project delivery. When asked which areas would benefit most from digitalization, 63% identify cost estimation, prediction, planning and control; 57% cite developing an asset lifecycle or “whole-of-life” perspective and 55% pointed to better progress monitoring. In each case, almost all agree upon the benefits of digitalization.

Yet actual adoption remains stubbornly low. In fact, the share of firms not using any digital technologies on projects increases each year, rising from 40 to 43% between 2021 and 2023. European firms believe in the value of digitalization but are now the least likely to use them in comparison to companies in the Americas, the Middle East or APAC.

Putting Value on a Paradox

So, how much does this contradiction between this belief and the lack of progress in practice cost European firms?

When trying to answer this question, one can focus on project performance. The Boston Consulting Group found that the use of digital technologies by E&C firms tended to reduce construction time by 15%-30% and cut lifetime costs by 20%.

But that’s only part of the equation. With several countries such as the UK, Germany, France and Spain mandating the use of BIM or digital twins in public-sector tenders, low digital adoption also represents loss of revenue. Lack of digital capabilities for project selection or project controls can also lead teams to avoid valuable projects by excess of pessimism or conservatism, or to submit uncompetitive bids because of poor cost or risk estimates and lack of benchmark data.

So how can forward-looking EPCs turn this situation to their advantage? The following are four concrete digital strategies that not only address the common barriers to adoption but also build a clear competitive edge over slow adopters.

#1 Leveraging AI to Strengthen Project Controls and Progress Measurement

When surveying large engineering and construction firms (for example, as part of our research into high-performing projects), one consistent finding is that firms fall into one of three groups. Roughly a third deliver on their time and budget commitments in 80% of their projects or more. Another third does so between 50-80% of the time.

This disparity highlights a profound challenge in project controls. Major projects across multiple sectors suffer massive financial losses due to inefficiencies, often stemming from inadequate control mechanisms. A 2021 study by KPMG revealed that major projects, on average, experienced $100 million USD in losses due to waste, with inadequate controls identified as the leading cause. This waste is often rooted in a reliance on manual data entry, subjective status reports and siloed spreadsheets, which collectively create poor visibility into true progress until it is too late to course-correct.

To address this pain point, leading EPCs are automating data capture and shifting toward approaches like Enterprise Project Performance (EPP) that can now be enriched with Artificial Intelligence. This shift replaces traditional subjective measurements, which often suffer from optimism bias and inconsistency, with objective data. For example, construction schedules are now being directly linked with real-time indicators such as quantities installed (via barcode or RFID scans) or actual work completed. AI and Machine Learning then analyze these patterns to predict potential outcomes, which allows for proactive intervention; for example, by flagging that a project is likely to slip a key milestone or suggesting more efficient resource allocation.

Platforms like Hexagon’s EcoSys exemplify this modern approach. As a leading EPP solution, EcoSys addresses poor progress visibility by integrating all key project functions, including scheduling, cost control and resource management, as well as connecting to diverse data sources like ERP systems and field applications. This integration establishes a single source of truth for project performance, injecting the necessary objectivity and consistency into decision-making.

It should be noted, however, that the shift requires more than just the software. True performance uplift is dependent on foundational organizational changes, including process standardization, robust data governance frameworks and a profound cultural shift toward data-driven decision-making across the organization.

When these systems are properly implemented, the impact on success rates is significant. According to a 2022 survey by Logikal, while only 5% of projects fully automate their project controls data, those projects report a 79% success rate in meeting targets.

#2 Collecting, Structuring and Contextualizing Project Data Within a Single Interface

Slow adopters tend to treat project data in a fragmentary way— each phase and department generates its own trove of emails, documents and spreadsheets that often vanish into archives after project close-out.

Three numbers demonstrate how doomed this strategy is: the World Economic Forum found that, on average, a single large infrastructure project today could produce 130 million emails, 55 million document and 12 million work orders. Keeping this volume of information buried in individual inboxes or disparate systems means thousands of hours wasted searching for information. It also leads workers to rely on memory or intuition rather than data.

Leading EPCs address this through integrated data ecosystems—a shift that requires both technological infrastructure and cultural change. These ecosystems are structured around platforms like digital thread or a project/asset digital twin. They spread information across traditionally separate domains (design, procurement, construction, operations).

For example, engineering models and tags can be linked to procurement statuses, which link to construction progress and then feed into an asset management system for operations. The result is greater speed and efficiency across the chain: for instance, when an engineering change occurs, an integrated system automatically notifies affected procurement orders, updates construction schedules and flags potential impacts on commissioning process that would take days or weeks within fragmented systems.

Contextualizing data like this means at any point in the lifecycle, the right people can access trusted, up-to-date information. This lifecycle approach also enables powerful analytics: AI can be applied to the unified dataset to find patterns (e.g. which contractors consistently perform better) or to forecast outcomes across the project portfolio.

Importantly, data connectivity needs to accompany the mere addition of tools. Without integration, adding more digital tools can ironically create extra work as teams jump between systems. It’s no surprise that 73% of EPC executives say lack of data integration has a “strong or severe” negative impact on their operations.

The high-performing EPCs avoid this trap by investing in platforms that speak to each other, often via standards like CFIHOS (Capital Facilities Information Handover Specification) for handover data and by enforcing data governance practices. The results are tangible: less time spent cobbling together reports and more time using information to make decisions.

#3 Avoiding Project Derailment at Commissioning and Handover

Another high-impact area where conventional practices fall short is completions, commissioning and handover to the owner/operator.

Traditionally, preparing turnover dossiers while ensuring every test, inspection and punch-list item is complete typically involves coordinating hundreds of documents across multiple contractors—a process prone to documentation gaps, version control issues and coordination failures between multiple stakeholders. These practices are also highly inefficient: as the Construction Industry Institute notes, “Commissioning failures are too common in frequency and extremely costly in impact. The business case for action is clear”.

So, what does this action look like? Modern completions management systems like Intergraph Smart® Completions turn this into a digital process. All asset information, checklists and test results are captured in a centralized database, with automated tracking Smart Completions of outstanding tasks. The payoff can be significant: on one Australian LNG megaproject, adoption cut the compilation and delivery time of commissioning dossiers by 98%.

Such tools also benefit the EPC in two important ways. First, commissioning is embedded in the design and construction processes rather than treated as an afterthought, which reduces rework, delays and other unforeseen issues during final testing. Second, they drive standardization, repeatability and continuous improvement rather than handling each project as one of a kind.

This emphasis on standardization is crucial: consistently delivering projects on time and on budget requires repeatable processes, not heroic efforts on each project. This consistency requires the effective use of data, from project selection to handover, to reduce risk and guesswork and increase margins.

The tools and methodologies exist and with digitalization adoption still lagging across much of the industry, early movers can establish a significant competitive advantage before digital maturity becomes a baseline expectation, rather than a differentiator.

Discover how Hexagon’s solutions, including project twins and EcoSys, our Enterprise Project Performance platform, can help you digitalize processes and achieve greater project performance here.

About the Author

Pedro Hidalgo Insua is a Senior Industry Consultant specializing in industry-leading solutions like S3D and SPI, SPEL, SP&ID, Smart Materials and construction tool. With a background in piping design, Pedro has over 20 years of experience in Information Management and Automation for the oil and gas and power industries, primarily in EPCs.

The post Three Ways EPCs Can Build a Competitive Advantage Against Slow-Digitalization Competitors appeared first on The European Business Review.

This approach is ideal for organizations that already possess mature data foundations and want to maximize the return on previous investments. In a composable setup, companies select the best tools for data ingestion, identity resolution, modeling, and activation, integrating them into a seamless workflow. The result is an agile, future-ready environment that adapts to business goals instead of forcing teams into rigid processes.

Why Businesses Are Moving Toward Composable Models

The rise of composable CDPs reflects a broader evolution in enterprise data strategy. Traditional CDPs once promised a “single source of truth” for customer information, giving marketing teams autonomy and reducing IT dependency. Yet, as data ecosystems matured, the weaknesses of monolithic systems became clear: duplicated data, rigid data models, slow updates, and vendor lock-in hindered flexibility.

A composable CDP, by contrast, builds on existing assets. Many modern enterprises already operate advanced data warehouses, pipelines, and governance tools, and simply need a smart activation layer to connect data to customer-facing applications. This shift transforms the CDP from a closed platform into an open framework that evolves alongside business priorities and customer expectations.

The Core Principles of a Composable CDP

At its foundation, a composable CDP rests on a few essential principles.

• The first is modularity: Every layer, from ingestion to activation, can be chosen and configured independently. This ensures flexibility and long-term scalability.
• The second principle is warehouse-native design, meaning that customer profiles and analytics models live directly in the company’s main data environment. This eliminates redundant storage, enhances consistency, and reduces costs.
• The third is governance and control. Because data remains within the organization’s secure systems, businesses maintain full transparency and compliance with privacy regulations.
• Finally, speed and efficiency distinguish the composable model. By leveraging existing technology, companies can deploy faster and realize measurable results in weeks rather than months.

How a Composable CDP Works

Imagine a business that already stores all customer interactions, transactions, and behavioral data in a cloud warehouse. A composable CDP connects this foundation to modular tools that unify identities, build segments, and sync them with marketing, sales, or customer-support platforms.

Data engineers oversee the pipelines and data quality, while marketing teams can directly create audiences or launch personalized campaigns. The data never leaves the warehouse unnecessarily, reducing duplication and maintaining real-time accuracy. This architecture bridges technical and business teams: Engineers provide structure and reliability, while marketers gain speed and autonomy.

It also supports rapid experimentation. New channels, products, or personalization strategies can be introduced without reengineering the entire system. Each component (whether for data collection, enrichment, or activation) can evolve independently, keeping the ecosystem resilient and adaptable.

Who Benefits Most?

Composable CDPs are especially valuable for organizations with established data infrastructure and in-house expertise—particularly in sectors like retail, finance, travel, and technology, where customer data is vast and diverse.

For marketing teams, this model delivers unmatched agility. They can design data-driven, omnichannel journeys using real-time insights without waiting for long IT projects. For data teams, it ensures alignment between analytics and activation, since both work from a single, reliable data source.

Smaller businesses with simpler needs may still prefer traditional CDPs. But as companies scale, the ability to customize, govern, and extend their systems becomes critical. That is where the composable model provides a distinct strategic edge.

Strategic and Organizational Impact

Implementing a composable CDP reshapes how departments collaborate. Marketing, data, and engineering teams align around shared objectives, creating transparency and efficiency across the organization.

Financially, the model is efficient and sustainable. Companies avoid overlapping tools, reduce redundant storage, and invest only in features that drive measurable value. The modular design supports gradual adoption. Businesses can begin with one or two high-impact cases and expand as results grow.

In the long term, composable CDPs help organizations stay agile in a constantly changing environment. As privacy laws, consumer behaviors, and technologies evolve, a modular architecture allows quick adaptation without major disruption.

The post Composable CDP: The Next Evolution in Customer Data Strategy appeared first on The European Business Review.

Here comes virtual data rooms as the best-fit solution.

A well-run data room is a secure environment for complex deals and due diligence.

Handling a reliable data room is your competitive leverage and strategic advantage.

The Changing Role of Data Rooms in Project Management

Due diligence data rooms like the Ansarada vdr make project management faster, easier, and safer.

Data rooms do more than merely keep records. It is now a space to communicate, collaborate, and make informed decisions.

Key Features of Data Room Project Management

Best Tips for Managing a Data Room

There are many best practices beyond the basic pillars to help you manage a data room project well.

• Start planning for the data room early: Start putting important documents in your data room when a transaction is about to happen.
• Choose a data room administrator: One person or a small group should be in charge of the data room to make sure that everything is consistent, safe, and up to date.
• Perform regular audits: Check the data room every so often to make sure the content and permissions are up to date and established.
• Use metrics: A lot of data room software comes with metrics that show how users are using the product. Find out which articles are most read, what people are interested in, and how engaged they are.
• Plan for diverse situations: Think about how to control access levels or data rooms for several bids or investors.
• Keep data room materials safe after the sale is over so that they may be used for compliance and future reference.

For more practical tips, visit https://datarooms.in/.

Wrapping up

In complex business deals, sound data room project management tools are becoming more and more in demand.

With the help of virtual data rooms, businesses handle complex deals by properly planning, structuring resources, enforcing security, and maximizing communications.

Private information is protected by VDR software solutions that accelerate the due diligence process and empower companies with a competitive advantage.

The post Data Room Project Management Strategies appeared first on The European Business Review.

The Rise of Big Data in European Markets

The expansion of big data in finance is driven by the sheer volume of transactions that occur daily across European exchanges. Every second, millions of data points are generated, ranging from price movements to order book changes and investor behavior patterns. Traditionally, firms struggled to capture and analyze this information effectively, leading to inefficiencies and blind spots in risk assessment.

Big data analytics has changed this landscape by providing tools that aggregate and analyze data at unprecedented speed and scale. Banks, asset managers, and trading firms now use predictive models and artificial intelligence to forecast market movements and detect anomalies. This capability has not only improved trading strategies but also enhanced compliance with Europe’s stringent regulatory frameworks, such as MiFID II and the Market Abuse Regulation (MAR).

Rewards of Embracing Big Data Analytics

One of the most significant advantages of big data analytics is its ability to uncover insights that were previously inaccessible. Market participants can identify trends, correlations, and risk exposures with greater precision. For example, liquidity patterns that might have gone unnoticed in the past can now inform more effective execution strategies and informed portfolio management decisions.

Another reward lies in compliance and risk reduction. Regulators in Europe have become increasingly strict in enforcing transparency and accountability. Big data analytics enables firms to monitor trades, communications, and patterns of behavior in real-time. This reduces the risk of market abuse, insider trading, and other forms of misconduct that could result in heavy fines and reputational damage. The proactive use of analytics provides not only operational efficiency but also a strong defense against regulatory scrutiny.

Supporting Regulatory Compliance Across Borders

European financial markets are diverse, spanning multiple countries with different legal systems, but they are increasingly harmonized by regulations. This complexity creates challenges for firms operating across borders. Big data analytics enables centralized monitoring and reporting, allowing firms to consistently meet regulatory obligations across different jurisdictions.

Advanced systems can integrate feeds from various exchanges and trading venues, ensuring that compliance teams have a unified view of trading activity. By leveraging these technologies, firms can demonstrate a commitment to transparency and accountability, which regulators view as a cornerstone of fair and stable markets. The ability to produce detailed audit trails further strengthens trust among stakeholders, from investors to policymakers.

The Risks and Limitations of Big Data Analytics

While the rewards are substantial, big data analytics is not without its risks. One of the key concerns is data quality. Poorly curated or incomplete data can lead to misleading results, compromising decision-making and exposing firms to unforeseen risks. The “garbage in, garbage out” principle applies strongly in this context, making data governance a critical priority.

Another challenge lies in overreliance on algorithms and models. While machine learning tools can process vast amounts of information quickly, they may also miss nuances or fail under unusual market conditions. Black box models, in particular, can raise concerns about explainability, a requirement that regulators are increasingly demanding. In periods of market stress, reliance on opaque systems could amplify rather than mitigate risk.

Cybersecurity and Privacy Considerations

The collection and analysis of massive datasets also expose firms to cybersecurity vulnerabilities. European markets are frequent targets for cybercriminals seeking to exploit sensitive financial information. Ensuring that big data platforms are secure and resilient is essential to protect both firms and their clients.

Privacy is another pressing issue. With the introduction of GDPR, firms must navigate strict rules governing the collection, storage, and use of personal data. Big data analytics must therefore balance innovation with compliance, ensuring that customer information is handled responsibly and ethically. Firms that fail to address these challenges risk both legal penalties and a loss of client trust.

Balancing Innovation with Human Oversight

Ultimately, big data analytics should not replace human judgment but rather enhance it. Traders, compliance officers, and risk managers need to interpret the outputs of analytical systems in light of broader market contexts and strategic goals. European firms are learning that the most effective approach combines cutting-edge analytics with experienced professionals who can exercise critical thinking and oversight.

As financial markets continue to evolve, the integration of human expertise with technological innovation will remain central to success. The firms that strike this balance will be better equipped to manage risks, capitalize on opportunities, and maintain resilience in an increasingly complex environment.

Conclusion

Big data analytics has become a cornerstone of European capital markets, offering clear rewards in terms of efficiency, compliance, and insight. At the same time, the risks associated with data quality, model transparency, and cybersecurity cannot be ignored. As regulators push for greater accountability, firms that adopt advanced systems responsibly will find themselves better positioned for long-term success. The future of trading and compliance in Europe will not be defined solely by technology but by the ability to use these tools wisely and ethically.

The post Big Data Analytics in European Capital Markets: Risks and Rewards appeared first on The European Business Review.

By Alan Jacobson

Analysts are shaping how organisations unlock measurable value from artificial intelligence. Alan Jacobson highlights their leadership in adopting AI and automation to drive strategic decisions, optimise performance, and enhance cross-functional collaboration. By following their example, businesses can embed AI as a shared capability, accelerating transformation and improving key performance outcomes.

Despite the growing hype around artificial intelligence (AI), only 39% of UK businesses are actively using it, according to Moneypenny’s 2025 Trends Report. This gap between interest and implementation is not due to a lack of ambition. I believe it’s due to a lack of an actionable AI strategy. Many leaders are still grappling with a fundamental question: how can we turn AI from a concept into measurable business value?

However, there is one role that is helping to buck the curve. We’re seeing data analysts emerge as AI frontrunners – not only in how they’re using the technology but in how they’re proving its worth. They’re demonstrating how to extract tangible business value from AI and, in doing so, setting a precedent for how it can be adopted across business functions. Let’s explore why.

Driving AI and automation adoption

Analysts have long been early adopters of new technologies. Their readiness for AI is reflected in our research on UK analysts’ adoption of AI and automation tools, The 2025 State of Data Analysts in the Age of AI, which revealed that 97% of UK analysts are already using AI and 87% are using analytics automation daily. They’re leading the charge on AI adoption because they understand how it will transform their jobs. Only 13% of UK analysts are “extremely concerned” about losing their jobs, and in complete contrast, 89% see AI as a career enabler.

As Gartner highlights, data and analytics professionals are central to AI success because they know how to turn complex data into strategic decisions and ensure AI initiatives deliver business value. The analysts we surveyed are already using AI to streamline data prep, extract insights at speed and improve stakeholder communication.

At the same time, analysts are applying these insights to strategic initiatives that align with KPIs and actively inform the decisions that influence them. Our research shows they’re using AI and automation to pinpoint opportunities for revenue generation and cost reduction, as well as to support key areas such as workforce planning, operational strategy, and financial management. As a result, 93% say AI has enhanced the perception of their role, positioning them as more strategic contributors within their organisations.

With analysts already on board with AI and automation, attention now turns to how their adoption can be replicated throughout other departments and organisations.

Follow the analyst’s lead

Many individuals across an organisation understand the overarching business goals – whether focused on growth, productivity, or innovation. However, beyond data analysts, few professionals have the tools or expertise to measure their performance against these targets. By following the lead of analysts – who are embracing AI and automation – other roles can gain a clearer understanding of their own KPIs and the AI-driven insights that can help track and improve them.

Analysts are not only at the forefront of AI and automation adoption; they’re also paving the way for other roles and functions to enhance their decision-making. This makes it a strategic priority for organisations to study how analysts are using AI tools, then scale those practices across teams – refining their application over time to drive better business outcomes.

To prepare other departments for AI and automation, organisations must first foster a culture of data and AI literacy. When teams understand the value of analytics, they’re better equipped to engage with AI tools, apply insights effectively, and contribute to smarter, data-informed decisions. This boosts individual and team productivity while also helping to generate continuous feedback on the impact of analytics investments.

Equally important are low-code and no-code platforms, which are transforming how organisations democratise access to data. These tools allow users to explore insights, automate workflows, and uncover key findings without needing deep technical expertise.

This shift is critical. It means that employees across finance, HR, marketing, and operations can independently analyse data, test hypotheses, and make evidence-based decisions, without relying on overburdened data analyst teams. As a result, organisations can scale their analytics capabilities more efficiently, reduce bottlenecks, and empower more people to contribute to performance improvements. Low-code platforms also support collaboration between technical and non-technical teams, ensuring that insights are generated and acted upon quickly and confidently.

Over time, the strategic value of investing in analytics and data infrastructure becomes increasingly evident. With the right tools and training, organisations can embed a culture of continuous improvement. One where every team is equipped to measure, understand, and influence the KPIs that matter most.

Make AI everyone’s business

By empowering more teams to use AI and automation in their own roles, analysts are showing businesses how those who don’t traditionally use data analytics tools can still make AI-driven decisions. This is where the real transformation happens: when AI becomes not just a specialist tool, but a shared capability across the organisation.

The result is a more agile, data-literate workforce – one that understands how to use AI and automation effectively and can demonstrate the ROI of their efforts. It also means that AI adoption is no longer siloed or experimental, but embedded in everyday decision-making.

To truly unlock the value of AI, organisations must follow the lead of their analysts: invest in accessible tools, foster a culture of data curiosity, and scale successful use cases across the business. When AI becomes everyone’s business, the impact on KPIs – and the bottom line – can be both measurable and transformative.

About the Author

Alan Jacobson is the Chief Data and Analytics Officer (CDAO) at Alteryx, where he leads the company’s data science initiatives and drives digital transformation for its global customer base. In this role, he oversees data management and governance, product and internal data, and the utilization of the Alteryx Platform to foster growth. 

The post Analysts are Redefining Business: Here’s why Every Organisation Should Follow their Lead appeared first on The European Business Review.

By Alan Jacobson 

According to research conducted by Alteryx, 82% of global business leaders say generative AI is significantly impacting their company goals and nearly half of board members are prioritising genAI over anything else. This is a massive tectonic shift in organisational strategy. And while many stories talk about the view that none of these very attractive genAI initiatives will work if they’re not built on a solid bedrock of AI-ready data, this isn’t the real impediment. What is the secret to making genAI drive value in an organisation? It turns out, it’s much the same as with all analytics: education for the organisation on how it all works – and finding use cases that are safe and easy to implement quickly to build muscle in this space.    

So, how can organisations build their muscle and drive results?  

GenAI models can be highly risky and problematic, or incredibly risk-free  

Ask a genAI model to file your taxes for you, and you likely will end up in a dark locked room that you won’t be getting out of any time soon. This is not only due to data quality, but the very nature of what Large Language Models are good at and what they aren’t capable of today.  

And while IT teams are focused on data quality with the report revealing that IT teams are confident with their data maturity and trustworthiness. Over half (54%) rate their data maturity as good or advanced, and 76% trust their data. On the surface, this sounds promising. But if you try executing this use case, it likely will not matter how good the data is.    

Instead, if you pick a use case to use genAI to highlight what new tax codes might impact the business and automatically e-mail these to the tax strategy team for review, you might immediately have a winning use case with very little risk. In this example, you are not dependent on your internal data quality, you are asking an LLM to summarise the mountains of news articles about new tax code, which is something LLMs are quite good at, and finally, you are not exposing or requesting any sensitive data to the LLM.  

There are a myriad of these easier and safer use cases that quickly allow organisations to build the muscle needed to succeed in the genAI space. But how do you identify these easier use cases? It really comes down to education. Similar to the education needed to harness the power of automation and more traditional analytics in your business, teams need to focus on upskilling and ensuring teams have the tools necessary to go on the data journey.  

Unfortunately, the report shows that only 10% of businesses claim to have a ‘modern’ data stack, and nearly half (47%) are currently working on updating their infrastructure. While new employees are learning these skills in universities, they are arriving into the workplace with little beyond Excel to perform analysis, let alone to leverage genAI.   

What key elements make up the modern data stack?  

Organisations need to have a set of technologies that allow the storage of data in a unified location (data lake), the ability for knowledge workers to manipulate data beyond Excel (data wrangling), to automate processes (automation) and perform analysis (analytics). These tools must be accessible and easy enough for the majority of knowledge workers to leverage so that data work is not the sole domain of the IT or Data Science teams. Unfortunately, where companies have historically invested has been in tools for their technology teams, with Python and other ‘code-first’ types of tools. While these help a small number of technical experts, these tools alone will not allow a business to go on the journey of harnessing the power of analytics and genAI.  And without bringing the business along, the use cases will likely continue to be suboptimal.  

As organisations build out their data stack, it is important to keep an eye on ROI. Building a data lake takes significant time and resources (e.g. data engineers) and will cost significant money. And unfortunately, the act of building a data lake by itself will not deliver significant ROI. ROI will come when applications, automation and analytics are delivered. These other types of technologies will take on two forms, centralised teams building solutions and democratised teams leveraging analytics and automation. In the former case, this means investing in people to centrally build solutions. This typically again takes significant investment and tends to focus on larger problems that have good ROI but take a while to deliver. You will put your top data scientists on big problems. Democratised ways of using these technologies will take much smaller investment, as you are not building a large team of dedicated resources to build solutions, but instead upskilling the people you already have. The goal is to make these people more efficient and with time being freed up, they can then drive to higher value delivery.    

Some companies get frustrated with an over investment early on data lakes with cost and returns not in-balance. Successful companies tend to drive fast returns with democratised analytics, and then re-invest a portion of these savings into their data lakes and centralised teams. They also benefit by democratising the analytics, as the business can now better articulate the priorities of what they need the data teams to deliver as well. In the end, the best data stacks are designed to deliver ROI every step of the way.  

Aligning budgets with the genAI opportunity  

Another challenge facing organisations is budget management. IT teams, in general, are responsible for data technology budgets, but the reality of how those budgets are allocated and adjusted tells a story that may have made genAI adoption difficult. Over half (54%) of businesses admit that budgets are not reviewed or adjusted throughout the year, even if new needs arise. Added to that, 54% say that if other priorities, projects, or spending needs arise after budgets are allocated, they cannot be adjusted. This proved to be a huge challenge last year when the pressure to adopt genAI grew exponentially.   

Given how quickly genAI has moved over the last couple of years and how quickly it continues to change, encouraging cross-functional collaboration and communication and updating how IT budgets are allocated or reviewed is vital. The current rigidity among organisations will have a big impact on innovation to the data stack and creating the right foundations for successful genAI use cases.     

Clearer horizons for enterprise-wide rollout    

While there are many challenges to achieving a modern data stack, organisations must focus on upskilling their workforce while putting the appropriate infrastructure in place to deliver analytics across the organisation. The key is to democratise the effort and ensure the teams are engaged and able to participate in the journey, not focusing only on technology for technologists. By addressing these challenges, organisations will be able to harness the full potential of genAI, driving innovation and achieving organisational goals.     

While companies are still at the early stages of seeing the full impact of genAI adoption, there is no doubt that the fundamental elements of analytic teams in the enterprise will shift, from simply building solutions to teaching the organisation and helping deliver the change management to upskill the workforce. Organisations must be prepared to drive this data literacy while navigating the age of genAI.

About the Author 

Alan Jacobson is the Chief Data and Analytics Officer (CDAO) at Alteryx, where he leads the company’s data science initiatives and drives digital transformation for its global customer base. In this role, he oversees data management and governance, product and internal data, and the utilization of the Alteryx Platform to foster growth. 

The post Overlook an AI-Ready Data Strategy at Your Peril   appeared first on The European Business Review.

Good business decisions depend on good information — it’s that simple. As companies collect more data than ever before, they face a real challenge: keeping all that information accurate, secure, and reliable. The businesses that handle this challenge well gain something invaluable: trust. When customers share personal details, they’re watching how you manage information. Today, how well you handle data matters just as much to your reputation as your products or customer service. 

The Risks of Poor Information Management 

Insufficient information leads to damaged trust, both inside and outside the organization, and can lead to poor choices because you’re working with flawed or contradictory data. 

Information biases affect processing in surprising ways. What does this mean? Simply put, we all have hidden preferences and assumptions that color how we collect, interpret, and use data across teams. Without good ways to counter these natural biases, you end up making mistakes that snowball over time. 

Your reputation takes a serious hit when information failures become public. You could lose market value, customer loyalty, and employee trust overnight. If you’re in banking, your stock price might plummet after a data breach. If you run a tech company, you might face furious customers after news breaks about your poor data practices. In healthcare, if you mishandle patient information, you’ll face stiff penalties and critically damage trust with your patients and providers for years to come.  

If you have strong data security practices, you can build a stronger reputation with your customers. This allows you to spot potential problems before they become crises, maintain stakeholder confidence during uncertain times, and consistently make smarter strategic decisions. 

Security Challenges in a Data-Driven World 

As you gather more data, security needs are likely growing much faster than your budgets, and this mismatch creates weak spots that threaten both daily operations and stakeholder trust. What worked for protecting information when a company was small often falls short as data volumes grow and systems become more connected. Implementing proper data consolidation can help reduce these weak spots by ensuring all information is organized, consistent, and easier to secure.

Don’t experience these growing pains too late — scaling your security is essential while businesses expand, but limited resources force tough choices. Security teams struggle to handle new types of data, different access patterns, and emerging threats while working with constrained budgets. If you’re expanding internationally, you face even greater hurdles, dealing with different rules and cultural expectations about data protection in each country.  

The two biggest problems are balancing security with usability while keeping costs manageable. Security measures that are too strict can hurt productivity when legitimate users can’t access the information they need. On the other hand, making things too convenient creates obvious risks. The answer isn’t choosing one over the other, but finding affordable, smart solutions that provide appropriate protection.  

To get ahead of these problems, AI-powered surveillance can be a powerful tool for your business. Video analytics can monitor facilities and data centers more efficiently than human guards, identifying potential threats before they cause damage. Multi-sensor camera systems provide comprehensive coverage with fewer devices, reducing both installation and maintenance costs.   

These intelligent systems check multiple factors before granting access, limit what users can see based on their needs, watch for unusual patterns, and automatically adjust security based on risk level. The most sensitive information gets stronger protection, while everyday data remains easily accessible to those who need it, all while making the most of limited security budgets. 

Tools and Strategies for Effective Data Management 

Good information handling needs both the right tools and the right company practices working together. You don’t rely on technology alone when building trust through smart data management — you combine powerful software with clear policies, regular maintenance, and human expertise to create systems that maintain data quality across your organization.  

Here are key practices that support effective information management:  

• Start with clear governance policies defining data ownership, quality standards, retention periods, and access controls before implementing any technical solution. 
• Product data management software like OpenBOM and Siemens Teamcenter provides essential capabilities for connecting information across departments while maintaining version control and audit trails. 
• Implement automated quality checks that apply consistent rules to catch errors before they spread throughout your systems. 
• Leverage machine learning algorithms to identify anomalies, detect potential duplicates, and predict emerging data quality issues. 
• Assign dedicated data specialists who understand both business context and technical requirements to bridge the gap between IT teams and operational departments. 

Data Privacy and Brand Trust 

Privacy has changed from just a legal box to check into a real competitive advantage – customers increasingly judge brands by how well they protect personal information, with many willing to pay more for products and services from companies they trust with their data. 

Research backs this up: data privacy impacts brand trust and customer loyalty across all industries and age groups. When you’re open about what data you collect, give your customers meaningful control options, and show ongoing commitment to privacy protection, you’ll see higher customer retention and more positive word-of-mouth. 

Following privacy laws is just your starting point, not your goal. While rules like GDPR in Europe and CCPA in California set legal requirements, when you’re building genuine trust, you need to go beyond the minimum. Build privacy into your products from the beginning rather than tacking it on later, discuss ethics when planning your data strategies, and regularly ask whether your data uses match customer expectations, not just legal requirements.  

Privacy-by-design helps you maintain this trust-centered approach. This means collecting only necessary data, keeping your information secure throughout its life cycle, providing clear user controls, and regularly deleting data that no longer serves your business purposes. When you let privacy considerations shape each stage of your product development and business operations, you’ll naturally build systems that respect user expectations. 

Executive Trust and AI Governance 

Leadership involvement is essential for building information trustworthiness across organizations. When executives show personal commitment to data integrity, security, and ethical use, these values spread throughout the company culture. On the other hand, when leaders bypass data rules or treat information ethics as just a compliance issue, employees notice and often do the same.  

The growth of AI makes this leadership example even more important because AI systems can greatly magnify both the benefits and risks of existing data practices. Executive trust in AI governance means finding the right balance between innovation and responsibility. When you actively help set AI guidelines and review processes, you’ll generally avoid the missteps that undermine stakeholder confidence.  

Effective executive involvement includes setting up clear oversight for AI and data systems. This means creating review boards with diverse viewpoints, regularly assessing how algorithms affect people, and being transparent about how automated systems make decisions. Leaders who understand both what their technology can do and the ethical questions it raises are better equipped to make more balanced decisions about acceptable uses. 

Having strong information governance typically involves bringing together teams that connect technical experts with business leaders and ethics specialists, with these teams developing guidelines for how data and AI systems should operate within the company’s values. They ask important questions about potential harms, unintended consequences, and whether specific applications align with stakeholders’ expectations. 

When you actively champion responsible data practices as an executive, you build a culture where ethical considerations become standard procedure rather than afterthoughts. Your employees at all levels will feel comfortable raising concerns about information use that might damage trust, creating an environment where your team identifies and addresses potential problems before they become crises. 

Final Thoughts 

Trust is your most valuable business asset, and how you handle information directly shapes that trust. When you manage your data with integrity, security, and ethical awareness, you make better decisions while avoiding reputation damage from mishandling information.   

Investing in sound information practices is essential for your competitive survival as your data volumes grow and AI becomes more prevalent in your operations. Your thoughtful policies, appropriate technologies, and your commitment as a leader create environments where your stakeholders confidently share information, knowing you’ll protect and use it responsibly.

About the Author 

Indiana Lee is a writer, reader, and jigsaw puzzle enthusiast from the Pacific Northwest. An expert on business operations, leadership, marketing, and lifestyle.

The post Building Trust in an Era of Data Overload: The Case for Better Information Management  appeared first on The European Business Review.

By Francis Rodino 

If you don’t truly understand your market, you’re shooting in the dark. Successful businesses thrive on market intelligence—knowing exactly who their customers are, what they want, and how they behave. This guide explores how to gain deeper market insights, outmanoeuvre competitors, and position yourself as the go-to authority in your industry. 

Why Market Leaders Obsess Over Their Audience 

Many businesses think they know their market, but assumptions don’t drive growth—data does. If you’re not constantly refining your understanding of your audience and competitors, you’re not standing still—you’re falling behind. In today’s rapidly changing landscape, staying relevant means evolving your offers, testing new strategies, and adapting to what the market actually wants—not what you assume it wants. A winning strategy starts with a structured, proactive approach to market research and positioning. 

1. Define Your Ideal Customer Avatar

To connect with your market, you need to know exactly who you’re speaking to and communicate in a way that resonates with them. This means going beyond basic demographics and understanding their pain points, desires, and decision-making process. When your message speaks directly to them, they’ll listen. 

Who are your ideal customers? What industry, niche, or profession do they belong to? Consider demographics—age, gender, income, education, and location—but also dig deeper into their psychographics. What do they value? What interests them? What challenges do they face daily? How do they make purchasing decisions? 

The clearer you define your audience, the more precise and effective your marketing becomes. Instead of casting a wide net, you’ll engage the right people who are already looking for your solutions. 

The best way to gain these insights is by speaking directly to your customers. Ask about their biggest challenges, frustrations, and what they wish existed to make their lives easier. Surveys and feedback loops can uncover hidden objections and unmet needs. Online communities—Facebook groups, LinkedIn discussions, Reddit threads—offer unfiltered insights into what your target market is thinking. 

Data analytics further refine your understanding. Look at website traffic, email engagement, and conversion rates. Which pages get the most visits? What offers get the best responses? Which ads perform best? Every interaction is a clue that helps sharpen your messaging and positioning. 

At the core of all successful businesses is one principle: the better you understand your customers, the better you can serve them. When your message aligns perfectly with their needs, they won’t just notice you—they’ll trust you. And in today’s competitive landscape, trust is the foundation of long-term success. 

2. Analyse Your Competitors

If you don’t know your competition inside out, you’re at a disadvantage. Identify your top three competitors and examine their strategies: 

• What promises and claims do they make? 
• How do they engage with their audience? 
• What are their pricing structures? 
• How do they differentiate themselves? 
• Why would your prospects choose them over you? 

Identify gaps in their approach and position your business to dominate where they fall short. Understanding what’s working (and what’s not) in your industry allows you to refine your own strategy and stand out in the marketplace. 

3. Assess Market Awareness and Sophistication

Not all customers are at the same stage of awareness when it comes to your product or service. Some don’t even realise they have a problem, while others are actively comparing options. Your messaging needs to align with their level of awareness: 

• Unaware: They don’t yet recognise the problem. Your job is to educate. 
• Problem-Aware: They know something’s wrong but aren’t sure of the solution. Focus on their pain points. 
• Solution-Aware: They’re researching options. Demonstrate why your solution is the best fit. 
• Product-Aware: They’re comparing competitors. Use social proof, case studies, and clear differentiators. 
• Most Aware: They just need the right offer or incentive to convert. 

Markets also evolve in sophistication. In an unsophisticated market, customers aren’t aware they need your service, so education is crucial. In a new sophisticated market, demand is growing, and thought leadership helps establish authority. In an established market, differentiation becomes essential. In a complex market with heavy competition, strong positioning and storytelling matter most. And in a saturated market, only innovation, brand loyalty, or exclusivity will set you apart. 

By understanding where your market falls on this spectrum, you can craft messages that truly resonate and drive conversions. 

4. Leverage Data to Drive Sustainable Growth

Businesses that capture and analyse data gain a significant competitive advantage. Instead of making decisions based on gut instinct, they use real insights to refine their strategies, optimise operations, and accelerate growth. 

By tracking website traffic, customer engagement, and sales patterns, you can pinpoint where your most valuable customers come from. Are they finding you through organic search, referrals, or paid ads? Understanding these trends allows you to invest in the highest-performing channels. 

Customer engagement metrics highlight what resonates most with your audience. Which content generates the most interest? What messaging leads to conversions? What types of posts spark conversations? Fine-tuning your marketing based on real behaviour ensures your efforts align with what your customers actually care about. 

Conversion rates and customer lifetime value provide deeper insights. How many leads turn into paying customers? What is their long-term value to your business? This data helps refine sales strategies, improve retention, and maximise revenue. 

Competitor analysis and market trends also play a crucial role. Monitoring shifts in customer behaviour and industry changes allows businesses to adapt quickly and seize opportunities before their competitors do. 

The fastest-growing companies aren’t just making better decisions—they’re making data-driven decisions. Knowing what works, what doesn’t, and where to focus ensures you scale with confidence. 

5. Test, Optimise, Repeat

Understanding your market isn’t a one-time task—it’s an ongoing cycle of testing, analysing, and refining. The best businesses don’t rely on assumptions; they continuously gather real data, identify patterns, and adjust their strategies accordingly. 

To truly understand your market, you must test different approaches. This could mean experimenting with pricing models, refining messaging based on customer feedback, or adjusting your services to better meet demand. Pay close attention to how your audience responds—what offers get the most engagement? What objections keep coming up? Where do leads drop off in the buying process? 

Every interaction with your market is an opportunity to learn. Surveys, customer interviews, and A/B testing reveal insights that help fine-tune your approach. Perhaps your ideal customers prefer a different communication style, or they prioritise features you hadn’t considered. The key is to remain adaptable. 

Markets evolve, and so must your strategy. The businesses that thrive are the ones that consistently analyse trends, optimise their offerings, and double down on what works. By treating market understanding as an ongoing process, you’ll stay ahead of the competition and position yourself for sustained success.

About the Author

Francis Rodino is an award-winning expert in sales automation and digital marketing, focused on helping SMEs thrive in the AI-powered era. With over 20 years of experience at the intersection of technology and marketing, Francis has led digital campaigns for global brands like PlayStation, Disney, and the Olympics, and helped Top Gear reach its first 10 million followers on Facebook. Now, as an international speaker and founder of Lead Hero AI, Francis helps SMEs leverage AI tools, marketing automation, and scalable strategies to generate leads, boost profits, and secure lasting success in today’s competitive digital landscape. He is also the author of Leads Machine. 

The post How to Better Understand Your Market to Drive Business Growth appeared first on The European Business Review.

By Alexander Igelsböck  

Five years from now, companies will reach a state of “data ubiquity” where data is hard-wired into all processes, interactions, and systems: at least, that’s what McKinsey predicts.   

Getting to this point will take some work, especially given ongoing progress against the consultancy’s last major forecast. By 2025, employees across sectors were meant to start enhancing everything they do based on data, in addition to expertly wielding smart machines. However, the continuing need for data leaders to build workforce data literacy suggests effortless daily data use isn’t advancing as quickly as hoped.  

So, while investment in new technology will keep growing this year, with 91% of global tech decision-makers due to boost IT spending, there will also be greater consideration of immediate values. More specifically, we can expect firms to zoom in on whether data management tools and practices are fuelling tangible benefits and moving them closer to meeting McKinsey’s data-empowered vision – and if not, why not? 

Making data more accessible

 Companies increasingly recognise that embedding data-driven working as standard means ensuring relevant information is freely available to the right people: a concept known as “data democratisation”. As noted by McKinsey, achieving that will involve mastering two core bases: making data easy to use and trust.   

For now, let’s focus on usability. Over recent years, many companies have fallen into the common trap of bringing in specialised solutions each time a new need crops up. Such piecemeal adoption, however, creates multiple issues. First, it can quickly lead to tech stack overload as more niche tools are leveraged, particularly with an ever-expanding range of analytics and intelligence tools on offer. Second, there is a high chance that isolated implementations won’t link smoothly to existing tech, meaning data isn’t actually simple for users to access or activate.  

In 2025, we will see more organisations looking at the wider data management picture. o. In general terms, that will include reviewing setups as a whole to determine if they cover all of the crucial l elements to drive efficient data handling, from collecting and connecting multi-source information to delivering neatly organised and analysis-ready datasets.   

In tandem with increased prioritisation of practical value,  additions to tech stacks will also become subject to sharper scrutiny. Looking beyond purely enticing features and their suitability for solving specific problems, firms will evaluate how potential new solutions can fit into current systems, and most importantly, whether they will fulfil promises of enabling simpler data use.   

Winning trust with persistent quality  

So, what about trust? Establishing slick data processing engines will ensure key components are in place to quickly get data where it’s needed. Yet amid fast-rising adoption of artificial intelligence (AI), there is also growing awareness that data-fuelled activity can be sent off course if the insights teams and tools are running on aren’t reliable. In fact, Gartner estimates one in three AI projects will fail this year due to subpar data, alongside several other issues.  

Unsurprisingly, data quality is becoming more important: recently ranked as the second biggest data and business intelligence trend for 2025, just behind data security and privacy. Over the next 12 months, increased interest in enhancing data trustworthiness will lead to a much stronger emphasis on tightening governance approaches. 

As part of wider data management assessment, companies will look at where improvements are needed to consistently safeguard data accuracy. During initial data sorting, for example, this might mean swapping any error-phone manual processing with automated cleansing and deduplication. When it comes to sustaining long-term data quality, firms may also opt to follow Gartner’s advice on frequently analysing the information they get from all data sources, in addition to embracing machine assistance to automatically check for and flag any suspect data.   

Companies are long past the time when data-supported proficiency was just a nice to have. In the volatile modern business environment, ensuring team decisions and smart tech outputs are informed by precise, relevant, and up-to-date insight is crucial to keeping performance on track and avoiding missteps. While the data maturity development of each organisation will always evolve at an individual pace, this means striving to attain McKinsey’s “data ubiquity” ideal is now a vital goal for all, as is enabling the usability and accessibility it requires.

About the Author 

Alexander Igelsböck is the Chief Executive Officer and Founder at Adverity. Since founding the business in 2016, he has been responsible for driving the growth and development of the company, as well as establishing a global presence of Adverity in the industry. Under his leadership Adverity has secured over $165m in seed funding and expanded into a global company with offices in Vienna, London, and New ork. Prior to joining Adverity, Alex was Managing Partner & Investment Committee member ati5invest. Now, he is an active member of the Forbes Technology Council and ICOM Global Advisory Board. 

The post What does McKinsey’s 2030 Vision Mean for Data Management this Year?   appeared first on The European Business Review.

Artificial intelligence technology has grown by leaps and bounds, and the global landscape is in the midst of an AI revolution. The trajectory and impact of AI are set in for the future. While AI has significant implications for academia, it is essential to remember that its ancestor, ELIZA, was developed as an exploratory tool, not to take on complete human adaptation.

There are many ethical considerations when adapting to and using AI, primarily when discussing AI girlfriend. However, it is a powerful tool in the right hands. It can identify research gaps across multiple disciplines and help researchers comb through volumes of academic literature in greater depth. Reports detail how students already use AI for their studies, which has broad-reaching implications for educational purposes and the world.

Ethical Considerations

Though many ethical considerations have been raised regarding the proper use of AI, developers have also addressed security and privacy. Ethical concerns have been raised about people’s dependence on technology and how this alters societal norms of human interaction.

In the fast-paced digital world, identity privacy is a significant concern for most. Protecting a user’s privacy, especially regarding data gathering and usage, is critical for AI developers. Security advancements have seen the advent of privacy policies that detail how a company collects, uses, and shares personal data.

Academically, plagiarism is a substantial concern when using AI. AI writing detectors flag potentially plagiarized works by analyzing the linguistic patterns of a text and predicting if it is AI or human-generated material. It is important to note that AI detectors are fallible because they rely on algorithms and training data programmed by humans, so a margin of error should be applied to the content, too.

Ethical considerations about AI and AI companions have been raised since its genesis, and developers have taken them seriously. AI users are also responsible for understanding the technology they are using and how to correct errors. One ethical consideration is that AI develops an “echo chamber” of information about a person’s beliefs and search habits. This can generate only one side of a view or idea. Ultimately, it is up to the user to research all aspects of an idea and make an informed decision.

Data Analysis

Data analysis is one of AI’s most profound benefits in academic research. AI analyzes large amounts of datasets extremely fast and uncovers insights that are difficult to identify using traditional methods. Embracing data-driven approaches using AI has become a critical part of the modern academic world, processing historical data to forecast future outcomes.

Since AI tools analyze large datasets quickly, educators can potentially identify gaps in the curriculum and areas needing improvement. The technology offers streamlined data, enabling educators to focus more on teaching than data management.

Predictive Analytics for Student Outcomes

Along with analyzing reams of data, AI has predictive analytics capabilities for predicting student outcomes. The information gleaned comes from current data trends, which help determine failures in courses or testing. The educator’s benefit is modifying lesson plans or scheduling tutoring for students who are predicted to fail classes or tests.

Using AI in Multiple Disciplines

ChatGPT is used in numerous academic disciplines. The use of technology in research, education, and access to knowledge has grown into a discipline that is becoming increasingly studied.

Since its release in 2022, ChatGPT has emerged as a valuable tool in preparing academic subjects because it can understand and respond to natural language inputs. It saves time and effort for researchers by performing tasks like creating article summaries and identifying key points for documentation.

Artificial intelligence is a wide-ranging tool and a valuable commodity across numerous academic disciplines. AI can be used in physics to explore predictions of mathematically intractable theories. Physics requires large amounts of data to be computed, and AI should ease the burden of finding patterns in larger datasets.

AI has the power to change the way that humans think about and teach psychology. It performs exceptionally well for predicting counterarguments in student papers and developing an answer beforehand. AI tools can also improve interventions and automate administrative tasks. The predictive models are staggering and facilitate assessing the mental health status of participants, particularly the pathological development of mental disorders like anxiety, depression, and stress.

Student Performance

The impetus behind AI transforming higher education is that it enables professors to monitor academic performance more accurately than in the past. Along with predicting how a student will perform, AI can help institutions proactively intervene with students before they fall too far behind, helping them stay enrolled and academically engaged.

Student Retention

Proactively helping students before they get too far behind is beneficial for the institution as it allows it to retain students. Institutions can also use predictive analytics to determine how many students will complete their studies. The implication behind this data is that AI can create a systemic change for institutions to support students throughout their academic careers.

AI technology has increased exponentially in just the past few years and will likely continue its trajectory into the foreseeable future. This has considerable implications for academia and, if the tool is harnessed correctly, could help it grow in never-before-seen ways. Artificial intelligence can analyze data that used to take years to research.

Faster processed datasets with accuracy frees up valuable resources and promotes efficiency so researchers and educators can focus their energy on other aspects of a project. AI also holds great potential for educators in predicting student outcomes and, along with the institution, can proactively help failing students before they start to fail.

Most ethical considerations have been responsibly handled, but it is ultimately up to the AI user to use this technology ethically. If used ethically, it can usher in a wave of academic progress that has never been seen before.

The post AI and Academia: Transforming the Future of Research and Data Analytics appeared first on The European Business Review.

Understanding Big Data’s Impact

Big data analytics refers to the sophisticated processing of vast datasets to uncover patterns, trends, and insights that were previously inaccessible. In the context of value investing, this technology offers a profound advantage, allowing investors to sift through an overwhelming amount of information at an unprecedented speed. The traditional methods of analyzing financial statements, market trends, and economic indicators are now augmented by real-time data streams, social media sentiment analysis, and complex predictive models.

Enhancing Fundamental Analysis

Fundamental analysis has been the cornerstone of value investing, involving a meticulous review of a company’s financial health, market position, and future growth prospects. Big data analytics elevates this process by integrating traditional financial metrics with a plethora of unstructured data from news articles, industry reports, and social media. This holistic view enables investors to gauge market sentiment, identify emerging trends, and assess the impact of external factors on a company’s valuation with greater accuracy.

Predictive Modeling and Value Discovery

One of the most transformative applications of big data in value investing is the development of predictive models. By employing machine learning algorithms, investors can analyze historical data patterns to forecast future price movements and identify undervalued stocks with higher precision. These models consider a wide array of variables, including macroeconomic indicators, consumer behavior, and geopolitical events, offering a more dynamic approach to value investing.

Risk Management and Portfolio Optimization

Big data analytics also plays a crucial role in risk management, a critical consideration for any value investor. Advanced analytics tools can simulate various market scenarios and stress test investment portfolios against potential downturns. This data-driven approach to risk assessment helps value investors make more informed decisions on asset allocation, diversification, and hedging strategies, ultimately optimizing portfolio performance in the face of market volatilities.

Real-Time Market Insights

The velocity at which financial markets operate today demands that investors stay ahead with real-time information. Big data analytics facilitates this by monitoring and analyzing market movements, news, and social media buzz as they happen. This capability allows value investors to react swiftly to market corrections, news events, or sudden shifts in investor sentiment, thereby capitalizing on short-term opportunities without losing sight of their long-term investment goals.

Ethical Considerations and Transparency

As with any powerful tool, the use of big data in investing comes with ethical considerations, particularly regarding privacy and data security. Value investors must navigate these waters carefully, ensuring that their data sources and analytical methods uphold the highest standards of integrity and transparency. Moreover, the reliance on algorithms and predictive models necessitates a clear understanding of their limitations and biases to avoid overreliance on quantitative data alone.

The Human Element

Despite the advancements in technology and the significant advantages offered by big data analytics, the human element remains irreplaceable in value investing. The interpretation of data, the understanding of market nuances, and the ethical considerations involved in investment decisions underscore the importance of human judgment. Big data serves as a tool to enhance, not replace, the insights and experience of seasoned value investors.

Bridging Technology and Tradition

The incorporation of big data analytics signifies a monumental leap forward. It’s a development that harmonizes the age-old principles of value investing with the cutting-edge capabilities of modern technology. To better understand this symbiotic relationship, we sought insights from industry leaders who are at the forefront of integrating these technological advancements into their investment strategies.

Vasyl Varkholyak, CTO of LaSoft, shared his thoughts on the intersection of big data and value investing. “The fusion of big data analytics with traditional value investing principles offers an unparalleled opportunity for investors,” Varkholyak explains. “It’s like giving a seasoned gardener a state-of-the-art set of tools. Not only can they cultivate their garden with greater precision, but they can also predict and mitigate potential threats before they impact growth.”

Varkholyak’s analogy underscores the profound impact of big data analytics on value investing. By equipping investors with sophisticated tools to analyze vast amounts of data, they are better positioned to identify undervalued assets and make informed decisions. “However,” Varkholyak cautions, “it’s essential to maintain a balance. While these tools offer significant advantages, the human element—our intuition, ethics, and understanding of market dynamics—plays a critical role in interpreting data and making final investment decisions.”

Looking Ahead

The integration of big data analytics into value investing marks a significant shift in how investors approach the search for undervalued assets. As technology continues to evolve, the capabilities and tools available to investors will undoubtedly expand, further blurring the lines between traditional investment strategies and modern, data-driven approaches. However, the core principles of value investing—patience, diligence, and a focus on intrinsic value—remain as relevant as ever.

In conclusion, the role of big data analytics in value investing is transformative, offering investors the ability to navigate the complexities of modern financial markets with greater confidence and precision. By leveraging these advanced technologies, value investors can enhance their traditional methodologies, uncover hidden opportunities, and achieve superior returns. Yet, the successful integration of big data into investment strategies requires a balanced approach, where technology complements, rather than dominates, the fundamental principles of value investing. As we look to the future, the synergy between big data and value investing will continue to evolve, shaping the next generation of investment strategies in an increasingly digital world.

The post The Role of Big Data Analytics in Value Investing appeared first on The European Business Review.

The location of your data storage is a crucial element of data security. Choosing whether to store your business data locally or to depend on international data centres can significantly impact security, accessibility, and compliance. This article delves into the strategic advantages of local data centres for businesses, the potential risks associated with overseas storage, and the benefits of collaborating with a reliable provider like Previder for sustained data protection and operational effectiveness.

Why Data Localisation Matters

Data localisation involves the practice of keeping business data confined to a particular geographical region, usually within the same country where a company conducts its operations. The significance of data localisation is paramount, as it is essential for maintaining regulatory compliance, enhancing security, and improving accessibility. Here are several important factors for businesses to think about local data centres:

1. Compliance with Data Protection Laws

Each nation possesses distinct data privacy and security regulations, and it is essential for businesses to adhere to these legal frameworks to steer clear of penalties and legal complications. For example:

• The General Data Protection Regulation (GDPR) in the European Union establishes rigors standards for data protection and privacy, detailing the conditions under which customer data must be stored and managed.
• In the United States, regulations like HIPAA for healthcare data and CCPA for consumer data protection establish rigors compliance requirements.
• Various areas implement comparable regulations that govern the storage and processing of sensitive data by businesses.

Opting for a local data centre allows businesses to adhere to national and regional regulations, effectively minimising legal risks linked to cross-border data transfers.

2. Improved Data Security & Privacy Protection

When businesses choose to store data in a foreign data centre, they must adhere to the legal regulations of that particular country. Certain governments possess regulations that enable them to examine, gather, or even confiscate data from foreign businesses without any advance warning. This raises significant concerns in nations with more lenient data privacy regulations.

Local data centres implement rigors security protocols, which encompass:

• Physical security: Advanced biometric access systems, round-the-clock surveillance, and dedicated on-site security staff.
• Cybersecurity measures: Firewalls, encryption, intrusion detection systems, and DDoS protection are essential components of a robust security strategy.
• Backup & disaster recovery: Local providers frequently deliver automated backups and real-time disaster recovery strategies, guaranteeing seamless business operations in the event of cyberattacks or system failures.

Collaborating with a local provider allows businesses to minimise their vulnerability to risks linked to foreign government surveillance and cyber threats.

3. Lower Latency and Faster Access to Data

The time it takes for data to travel between a user and a data centre, known as latency, can greatly influence business operations. When data is stored in international locations, access times can be impacted by:

• Increased network distance
• Congested international data routes
• Unpredictable connectivity issues

Storing data near your business location guarantees quicker access, smooth cloud application functionality, and enhanced real-time data processing.

4. Better Disaster Recovery and Business Continuity

An effectively organised disaster recovery plan is essential for businesses that cannot tolerate interruptions caused by cyberattacks, system failures, or natural disasters. Regional data hubs:

• Offer redundant power supplies
• Implement multi-location backups
• Provide instant recovery options in case of data loss

Moreover, keeping data stored locally helps avoid potential conflicts related to data sovereignty that can occur during cross-border data recovery.

Risks of Overseas Data Centres

Although companies might explore the option of utilising overseas data centres for their cost-effectiveness or enhanced scalability, this choice carries significant risks. Here’s how depending on overseas storage can negatively impact your business:

1. Foreign Laws and Government Access to Data

Although companies might explore the option of utilising overseas data centres for their cost-effectiveness or enhanced scalability, this choice carries significant risks. Here’s how depending on overseas storage can negatively impact your business:

• The U.S. CLOUD Act (2018) empowers U.S. authorities to request data from any company subject to U.S. jurisdiction, irrespective of the physical location of the data.
• China’s Cybersecurity Law (2017) mandates that all essential business data gathered in China must be stored domestically, thereby restricting foreign oversight of sensitive information.

The legal risks present significant challenges for companies managing confidential information, leading to local storage being a more secure choice.

2. Political and Economic Uncertainty

Due to cheaper operating costs, many companies store data in developing economies; nonetheless, these areas may experience economic crises, political unrest, or abrupt legislative changes, all of which can:

• Disrupt business operations
• Compromise long-term data security
• Lead to unexpected service disruptions

3. Communication and Cultural Barriers

Dealing with time zone variations, cultural misalignments, and language problems while working with a foreign data centre may lead to:

• Miscommunication during crisis situations
• Delayed support response times
• Complicated troubleshooting procedures

By providing direct connection, real-time assistance, and culturally appropriate solutions that are customised to your company’s requirements, a local supplier removes these concerns.

Why Choose a Local Partner Like Previder?

Businesses must prioritise dependability, security, and compliance above cost when choosing a data centre. Benefits of working with a reputable local partner like Previder extend beyond simple data storage and include:

1. Regulatory Compliance & Data Sovereignty

Businesses who use Previder ensure that they satisfy industry-specific and national data protection needs while also guaranteeing compliance with local rules.

2. State-of-the-Art Security Infrastructure

Previder’sdata centres offer:

• Multi-layered security protections
• Encrypted data storage solutions
• Round-the-clock monitoring to prevent breaches

3. Customised Business Solutions

Local data centres provide custom processing and storage solutions, in contrast to global companies that offer one-size-fits-all packages. Companies may increase or decrease their storage requirements without incurring excessive costs for resources that are not utilised.

4. 24/7 Personalised Support

Businesses that work with a local partner get committed customer service in their mother tongue, with prompt help when required.

5. Improved Network Performance

Faster access speeds, less latency problems, and smooth cloud-based operations are all guaranteed by a local data centre, and these factors all improve corporate productivity.

Make a Conscious Choice for Secure Data Storage

Making the incorrect data localisation decision may have long-term effects for your company, impacting everything from security and performance to compliance. Selecting a local data centre is one of the most important choices a company can make given the increasing dangers from cyberspace, government requirements, and latency issues.

Businesses who use a reputable local supplier like Previder can:

• Ensure regulatory compliance
• Protect sensitive business data
• Gain faster and more reliable access to critical information
• Receive customised, scalable solutions tailored to their needs

One of your most important assets is your company’s data; make sure it’s locally, securely, and safely kept.

Discover the advantages of a reputable local data centre right now.

The post Is Your Business Data Safely Stored? The Importance of Local Data Centres appeared first on The European Business Review.

NJM Insurance is a regional property and casualty insurance company. For personal customers, it offers auto, homeowners, renters, condo, and umbrella insurance. For commercial businesses, it offers workers’ compensation, commercial auto, commercial package, businessowner and commercial umbrella policies.

NJM Insurance is a regional property and casualty insurance company. For personal customers, it offers auto, homeowners, renters, condo, and umbrella insurance. For commercial businesses, it offers workers’ compensation, commercial auto, commercial package, businessowner and commercial umbrella policies.

The NJM marketing team needed to analyze ad campaign performance across more than 10 platforms to understand the impact and related customer lifetime value of each campaign. Aggregating this data presented numerous challenges, chiefly the time and application API knowledge needed to build data pipelines into NJM’s data lakehouse. Facing a potential 9-to-12-month project timeline, NJM’s data team turned to CData Sync, which offered a solution not only to marketing analysis but also to a reorientation of how the business thought about data and analysis.

The challenge: Data integration timeline stretching to 300+ days

The marketing team at NJM had expanded its strategy into a broader range of digital advertising platforms. To understand the impact of its ad spend on each platform, NJM needed to verify how the audience aligned with its target, whether that audience exchanges further with NJM, and ultimately whether those who saw ads became customers. But as data engineering colleagues Felix Muñoz and Ameya Narvekar soon realized, the variety of data points and formatting via API endpoints in applications like Google Ads, Facebook Ads, and YouTube was nearly endless.

Extracting the necessary data from each application necessitates deep knowledge of each API, requiring poring over tedious documentation for each pipeline. Muñoz and Narvekar built two test integrations and wound up spending two months on the project, attempting to get the permissions correct, standardize data table formats, and map to their data lakehouse destination. To make available all the data that marketing requested, it would take an estimated 200-300 days based on the manual in-house process.

At that point, Narvekar and Muñoz began investigating a software solution that could provide them the data connections to all the needed ad platforms right out of the box. Ready-to-go connectors would make the marketing team’s request seem reasonable to fulfill.

The solution: Rapid pipeline deployment with CData Sync

To solve the marketing connection dilemma, the NJM data team decided it needed a scalable solution that would solve not only the current connectivity problems but also future integration needs. Above all, NJM needed to dramatically accelerate its project-delivery timeline. The team additionally sought out data transformation capability, technology agnosticism, and cost-effectiveness.

Sync proved the right fit because it met several core criteria. Sync’s low-code/no-code interface meant that NJM didn’t have to hunt through API docs to configure any connections, providing the enhanced time-to-value they needed. Sync’s structure of providing replicated data as SQL tables gave Muñoz and his team a format they knew well and could standardize on. Additionally, knowing they would need to move billions of data rows monthly, Sync’s fixed-cost pricing model based on the number of connections meant they could scale their efforts without exponentially increasing their spend.

With Sync, the NJM marketing team gets all of its ad data aggregated in reports to analyze impact. NJM is also able to ingest policy and claims data into its lakehouse for analysis, as well as other third-party data. This enables it to see a full picture of business performance and better serve customers.

The outcome: From 300 days to integrate down to 20

The impact of adopting Sync at NJM was substantial and immediate. The data team’s estimate of 200-300 days to build the necessary marketing integrations fell to 20-30 days, meaning Sync enabled those pipelines to deploy 10 times faster. And compared to the developer time and processing, using Sync costs NJM 66% less than building the pipelines manually.

With these efficiencies, NJM has been able to accelerate pipeline builds and now uses more than 20 connections within Sync.

“It takes 4 hours max for any client to get up and running, and we can start looking at the data.” – Ameya Narvekar, Data Insights Supervisor, NJM

With the efficiency and flexibility of Sync, NJM is planning numerous other data projects to maximize the value of its data. Within data science, the team is looking at ingesting more third-party data like weather patterns, better resolve data gaps, and enhance fraud-detection efforts. Within its usage-based insurance offering, the team aims to power real-time risk assessment, geographic risk avoidance, and enhanced customer engagement.

To learn more about NJM’s story, watch the team’s presentation from CData Foundations 2024.

Make better use of your data to drive business with CData Sync

CData allows organizations of any size to connect their entire data stack, providing timely, accurate information based on real-time data. CData Sync provides data integration pipelines from any source to any application – in the cloud or on-premises. Want to test it out? Get a 30-day free trial today.

The post NJM Insurance Builds Data Pipelines 10x Faster to Analyze Marketing Impact appeared first on The European Business Review.

As one of the most recognizable names in hospitality, Holiday Inn Club delivers luxurious vacation experiences across 31 United States resorts. As a data-driven organization, they leverage multiple databases and business applications to track, engage, and evolve their complex marketing, sales, and financial processes. But over the past 10 years, Holiday Inn Club has seen increased volumes, transactions, and demands for data – putting strain on their existing infrastructure.

Limitations with their legacy IT systems led to multiple outages and system reboots every day, so Holiday Inn Club decided to migrate their customer-centric operations to Salesforce. The goal was to support organizational growth and better surface the massive volumes of transactional data for finance, sales, and marketing departments to analyze.

The problem? Holiday Inn didn’t have a good way of getting their data from Salesforce to their reporting platforms for business intelligence initiatives. They needed a reliable data replication solution to synchronize more than 20 million records from their new Salesforce instance to their various on-prem and cloud data warehouses to enable robust near real-time reporting.

After combing through various integration offerings on the market to find the right solution, Toledo found CData Sync – and had his solution up and running in production in just two weeks.

The CData data integration solution virtually eliminated the need for troubleshooting and manual data retrieval. Now, various departments across Holiday Inn Club can access and analyze up-to-date customer and account data from their preferred reporting platforms.

The Challenge: Organizational Growth Leads to Data Disruption

The IT team at Holiday Inn Club struggled with the limitations of their SQL database. Increased daily transactions and requests forced them to have to reset their servers every few hours, as they became sluggish under high-volume workloads. Data retrieval delays were starting to affect Toledo’s ability to provide timely insights into sales commissions, marketing projections, and payments information.

After standardizing on Salesforce for operations, Toledo urgently needed a robust data replication solution to get huge amounts Salesforce Service Cloud data into his Azure and on-prem SQL servers for use across multiple departments.

“We tried to use SSIS and Azure Data Factory and none of those worked as we wanted them to,” said Toledo. “We had issues with watermarks and configurations and ended up with a huge gap in data because the jobs took so long to run. That’s when we decided to start discovery for a tool that allowed us to do huge synchronization jobs in near-real-time.”

The Solution: Reliable Data Replication in Just Two Weeks

CData Sync stood apart in Toledo’s discovery process as the only tool that provided a successful proof-of-concept (POC) for Holiday Inn Club. In just two weeks, Toledo had a data integration solution running in production, and his end users were quickly relying on CData Sync to drive their initiatives forward.

“I’m really impressed with the technology, how it actually works, and the thought that went into every single one of the options you can find when you go to the GUI (graphical user interface),” said Toledo. “I wanted to move [my data] from Point A to Point B and create these connections, and all of the options for that are laid out for me. You can also see how you can expand those connections when you’re building that synchronization process. It’s really amazing to me.”

“I’m really impressed with the technology, how it actually works, and the thought that went into every single one of the options you can find when you go to the [interface].” – Irving Toledo, Holiday Inn Club Vacations Senior Software Architect

“Another good feature that we like about CData is the fact that you don’t store any data,” Toledo expanded. “You’re not compromising my data the way other vendors might by taking it from the source and moving it to their own cloud, then moving back to the destination. So, the fact that we can put our data on-prem in our own servers or put it in Azure – it’s my choice. I like the flexibility there.”

CData Sync is now a core component of Holiday Inn Club’s data ecosystem – replicating massive volumes of Salesforce data every other minute into their Azure cloud database and SQL Server instance on-premises. End-users across finance, sales, and marketing can now access all the data they care about for holistic reporting in Power BI without worrying about delays, errors, or outages.

“I can sleep again, knowing that the replication is working. If I stopped CData Sync today, I’d get flooded with calls from my teams in the next 20 minutes. The near-real-time data we get with Sync has transformed how we work in a big way.” – Irving Toledo, Holiday Inn Club Vacations Senior Software Architect

Now, it’s simple for Holiday Inn Club to reliably calculate sales projections, track outstanding payments for vacation packages, optimize marketing campaigns, and provide seamless experiences for their timeshare owners.

Toledo has been so pleased with their Salesforce replication that they have continued to integrate other systems. For instance, Holiday Inn Club’s email marketing campaigns reach millions of people every month, generating massive monthly Salesforce data volumes of around five to six million records. To handle this workload, Toledo is now using Sync to replicate data from Salesforce Marketing Cloud.

Toledo is also expanding his use case with Sync even further, conducting a POC to replicate data from Holiday Inn Club’s Oracle-based mortgage platform, Daybreak, as well.

“Right now, we have an SSIS job that consistently fails to fetch the Oracle data and move it to our SQL Server,” said Toledo. “Basically, Sync will help us avoid the situation of having to reset and fix retrieval jobs by allowing me to pull the data down under a different schema, so everyone’s fetching data as normal — and I don’t have to change anything major.”

Visit www.cdata.com to learn more about CData solutions.

The post Holiday Inn Club Rests Easy with Error-Free Salesforce Data Movement appeared first on The European Business Review.

Modern organizations across all sectors are grappling with the challenge of harnessing the full potential of their data. The presentations from three organizations in the Data Engineering track at CData’s inaugural Foundations event crystallized several key themes that span across industries and data use cases.

In their sessions, data professionals at NJM Insurance, Commando, and the World Wildlife Fund shed light on the common struggles and how innovative solutions are made possible by effective data integration and replication. This blog pieces out their shared challenges and eventual solutions to equip colleagues to be more efficient data consumers, saving time and money.

The data dilemma: a universal challenge

Regardless of industry or size, businesses are facing similar hurdles when it comes to data integration. The primary barrier is unifying information from a multitude of sources and systems. NJM, for instance, found itself juggling various advertising applications, while apparel manufacturer Commando wrestled with multiple internal systems like BlueCherry ERP, Shopify, and Centric.

This fragmentation of data across different platforms is a significant barrier to efficient decision-making and predictable business growth. For Commando, incomplete data jeopardized order fulfillment accuracy and customer retention, presenting a significant risk to sales targets.

The rise of self-service data solutions

One of the most striking trends emerging from these case studies is the shift toward self-service data solutions. All three speakers emphasized the importance of empowering non-technical teams with the ability to access and utilize data independent of IT oversight.

Democratizing data access can yield significant results—and in a hurry. It allows marketing teams to pull customer insights without waiting for IT support, enables finance departments to generate real-time reports, and empowers product teams to make data-driven decisions on the fly. The result is a more agile, responsive organization that can quickly adapt to market changes and customer needs.

A low-code tool at a fixed cost

In evaluating potential software solutions, the trio of organizations all sought a user-friendly interface and low-code/no-code approach. Not only would these features make data integration and replication accessible to team members across various technical skill levels, they would also accelerate time to value.

The impact was immediate and significant. NJM reported a 90% reduction in time spent gathering data while incurring only 1/3 of the cost compared to its manual pipelines. Commando, too, saw marked improvements in efficiency and decision-making processes. These outcomes underscore the transformative potential of the right data integration tool.

Beyond integration: ensuring data quality and consistency

While connecting disparate data sources is crucial, it’s only part of the equation. NJM’s focus on maintaining data quality and consistency across all its sources highlights another important aspect of effective data management. After all, integrated data is only as valuable as it is accurate and reliable.

This emphasis on data integrity is a reminder that data integration reached beyond consolidation—it also creates a trustworthy foundation for business intelligence and strategic decision-making.

All three organizations are using their data in ways they hadn’t predicted since they implemented CData Sync to manage their data pipelines. Once Commando began integrating its core business systems, it realized that it could streamline product labeling and simplify its shipments to retailers.

Looking ahead: data integration + AI

As these organizations look to the future, the journey of data integration continues to evolve. Commando and NJM’s exploration of AI technologies to further leverage their integrated data points are aimed at improving customer options and overall satisfaction.

At WWF, new ways of resource-sharing are on the horizon, with an eye toward data-driven recommendations for conservation teams working in the field.

To learn more about CData Sync, take a product tour by visiting www.cdata.com/sync/demo or sign up for a free 30-day trial.

The post Foundations 2024: Key Takeaways from Data Engineering Leaders appeared first on The European Business Review.

CData is honored to be recognized in the 2024 Gartner® Magic Quadrant for Data Integration Tools. We are especially proud to be the only new vendor included among the 20 leading providers in this evaluation.

We want to thank each of our 7,000+ direct customers and 150+ OEM customers for your support in reaching this milestone. We’ve drawn directly from your feedback in our community to fuel our innovation across 45 product releases in 2024 alone. Thank you for your partnership, your feedback, and your time. You have shaped CData as a platform and as a company and we are grateful for your investment in us. 

This acknowledgement is personally exciting for me as a validation of our work at CData building a connectivity platform that solves complex challenges for our customers. We feel it also underscores our evolution from a connector company to a trusted vendor in enterprise data integration. 

We know that building on our foundation of connectivity sets us apart from typical integration providers and lets us offer our customers the unique capabilities they need to build a solid data foundation for their businesses. 

Get the Full 2024 Gartner Magic Quadrant Data Integration Report. 

Why CData was recognized 

Our recognition in the Magic Quadrant highlights our strong execution and visionary approach across four products in our portfolio: 

• Sync: ETL/ELT pipelines to replicate any data source to any database or warehouse 
• Connect Cloud: Centralized SaaS platform for governed self-service access to live data in the cloud 
• Virtuality: Enterprise-grade semantic layer 
• Arc: Comprehensive, no-code EDI and MFT 

These four products are built on our best-in-class connectivity – with 300+ sources and destinations – and cover a range of integration patterns and methods needed by today’s enterprises to reliably manage their data at scale. Because our platform was built on this industry-leading connectivity foundation, we support both data movement and live data access – capabilities also recognized in the 2024 Gartner Critical Capabilities for Data Integration Tools. 

With this connectivity foundation, CData can:

• Deliver industry-leading connectivity to enterprises: We offer an unmatched range of depth and breadth in connectivity that scales to cover complex enterprise environments. As a proof point of that range, we provide embedded connectivity for several software vendors like Salesforce, Google Cloud, Atlassian, UiPath, and Collibra – and even powers connectors for other data integration vendors included in the Magic Quadrant. 
• Provide low total cost of ownership (TCO): With native connectivity, our integration products are lower effort to set up and maintain. Because of our highly efficient R&D model, we can also offer unmatched price-to-performance and scalable pricing for customers. 
• Offer both live data access and data movement capabilities: In 2024, we acquired Data Virtuality, adding robust virtualization capabilities to our platform. In contrast with other vendors who support only one integration pattern like ETL/ELT, CData’s platform can support both live data access and data movement integration patterns needed to support enterprise-wide strategies of data and analytics leaders. 

We feel this recognition signifies a clear validation for our approach in a highly competitive and mature market. But this isn’t just about where we stand today—it signals our role as a strong partner to our customers, elevating what they can expect from data integration tools. 

Why it matters 

Based on a recent study by Salesforce, the average enterprise manages 1,000+ systems and that number continues to grow at 25% every year. With an exploding variety of systems and greater demands to access that data across the business, companies need an integration platform that can both connect and integrate data across all the possible source and destination pairings in combination with users’ access needs. 

Traditional vendors are challenged to meet the wide range of connectivity and data integration patterns required to meet this changing landscape. On top of that, as AI permeates into the business world, the need for a solid data foundation is becoming ever more urgent. 

As a result, businesses are turning to data integration vendors that can stand up to the sprawl of systems and data with scalable solutions and sustainable pricing. 

Building on this recognition 

We aim to evolve CData’s platform to fill this market gap and build upon our acknowledgement in Gartner’s report. And we will do that through a relentless focus on unmatched value for our customers. 

We are uniquely positioned to do this for our customers in part because of our underlying connector technology but also because of our efficient operating model. 

As a bootstrapped business building our connector catalogue, we found a way to leverage a common connectivity platform across all our products. As we’ve scaled, this gives us tremendous leverage with our R&D and allows us to be extremely efficient in our product development. We translate that efficiency into a highly performant platform at a sustainable cost for our customers. Our operating model combined with our recent $350M investment means we can keep rapidly innovating and delivering for our customers. 

We are proud to offer unmatched price-to-performance, honored to be recognized in the Data Integration space, and excited to continue to partner with our customers to build products that make a difference for their businesses. 

Gartner Disclaimer 

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. 

The post CData Recognized in the 2024 Gartner Magic Quadrant for Data Integration Tools appeared first on The European Business Review.

By Chengyi Lin

The last half-century has been characterized by our unceasing drive to assimilate ever-evolving digital technology into every aspect of our lives. The Paris 2024 Olympic Games provided a revealing example of the huge benefits that getting it right can yield.

The Olympic Games is the world’s most celebrated sporting event. The recently concluded Paris 2024 Games hosted more than 10,000 athletes from 206 National Olympic Committees and attracted more than half¹ of the world’s population to watch the Games as they unfolded over the course of nearly three weeks.

The Paris 2024 Olympic Games provided a revealing example of the huge benefits that getting it right can yield.

Paris 2024 achieved a critical milestone for the Olympic Agenda 2020+5,² which tries to drive more progress to transform the Olympics for youth and the new era. What is the role of digital technologies in this critical transformation? How can we ensure that the Olympics engage with the younger generation who are “digital natives” and value instantaneous communication through various digital channels? Our ongoing research with the key Worldwide Olympic Partners (WOP) examined the last four games: the Pyeongchang 2018 Winter Games, the Tokyo 2020 Olympics, the Beijing 2022 Winter Games, and Paris 2024. We went deeply into the behind-the-scenes technology services to understand how digital delivers value to the Olympic transformation.

Considering that the Olympic Games operate in a high-pressure, high-visibility and high-expectations environment – one that is filled with complexity, uncertainty, and intensity – these lessons are valuable to different types of business around the world who are trying to make technologies work for their own transformation.

Focus on Technology Reliability to Conserve Value

In 2024 alone, the world was subjected to a glut of digital failures. Among the most visible ones were the Meta server outage³ that caused Facebook and Instagram to be down for more than two hours globally, as well as the Microsoft crash in which an outage in its CrowdStrike cybersecurity software affected over 8.5 million⁴ Windows devices worldwide. Even tech giants cannot guarantee the absolute reliability of their technologies.

This is unacceptable for the Olympic Games. As we witnessed during the men’s 100-meter race, American runner Noah Lyles won the gold medal by a 0.005-second margin⁵. Omega’s technology needs to be extremely accurate and reliable to ensure these results. Actually, to eliminate the frustrations and chaos caused by any technological outages, the International Olympic Committee set a nearly impossible goal: that essential technology services, including cloud and timekeeping, should run successfully 99.999 per cent of the time.

How can the technology providers, such as Alibaba, Atos, Deloitte, and Intel, deliver on this near-perfect promise given the complexity, uncertainty, and intensity of the Olympic Games? The answer is redundancies and testing.

First, all teams built sufficient technology backup systems and dedicated support teams. For example, one from the WOP Alibaba Group built sufficient backup systems around their cloud technologies, ranging from hardware to software. They established multi-copies of local storages and built in both active-backup and active-active instances⁶ of relational databases. Additionally, Alibaba used load balancers to distribute traffic to multiple back-end servers. To provide sufficient multi-level redundancies without blowing up the budget, Alibaba Cloud also leveraged existing servers from within and cross regions. This also avoided adding headcount to the new server sites.

The International Olympic Committee set a nearly impossible goal: that essential technology services, including cloud and timekeeping, should run successfully 99.999 per cent of the time.

Testing through scenario simulations is critical to make sure the redundancies and the process work well under stress. To do this, long-term WOP Atos built a 1,000-square-meter Integrative Testing Lab⁷ in Madrid, and completed over 250,000 hours of testing and simulations before June 2024. Similarly, Alibaba also ran five end-to-end rehearsals, including three internal ones for continuous improvements and two technical rehearsals, with the Paris Organising Committee and other technology partners such as Atos, Intel, and Samsung. Intel’s digital twin platform⁸ also allowed event planners to access the simulations remotely from other parts of the world, so that they didn’t need to travel to Paris multiple times but could still rehearse and improve the events simultaneously.

Although these back-end efforts were all hidden from the public eye, they provided the foundation for a smooth and frustration-free experience.

The 99.999 per cent reliability of the technology not only ensured that little to no value was destroyed through mistakes, accidents, and any other uncertainties, more importantly it helped to save tremendous costs for the games. For business leaders, one lesson from the Paris 2024 Olympics is that digital technologies could allow all stakeholders, including the athletes, hosts, volunteers, broadcasters, event planners, team supporting staff, and many more, all to share the same synchronized information, which significantly reduced the chance of crises, thus conserving value for all the end users.

Guide AI to Improve on Quality, Not Just Quantity

Since the launch of ChatGPT in November 2023, generative artificial intelligence (GenAI) has been helping content creators, such as marketers,  to generate various versions of the same idea. The GenAI-generated content may vary in quality, but it compensates in quantity and speed.

The Olympics actually require the opposite use case: less quantity but higher quality.

With 10,714 athletes competing in 329 games across 32 sports⁹, the Olympic Games organizer hosts an overwhelming quantity of broadcasting contents in the cloud. How different media outlets are able to select the right ones in real time to broadcast through multiple streaming channels is a significant challenge. This is where AI, and GenAI in particular, stepped in.

Such efforts actually started with the Tokyo 2020 Games. Historically, before 2020, broadcasters, such as France Télévisions, NBC, and CCTV, would send their own crew to record a specific match, athlete, or angle of interest. For example, for Rio 2016, NBC sent over 2,000 staff¹⁰, including anchors, reporters, editors, camera crew, etc. The BBC, after criticism, sent a reduced number of 455¹¹ for the same year.

Not only are the proprietary videos and images captured not shared with other broadcasters, generating a lot of waste, the overwhelming quantity of recordings can take days or even weeks to sift through. They are also difficult to fit into the various formats of social media platforms.

The Tokyo 2020 Games became the first Olympic Games to be migrated into the cloud.

Cloud technologies help tremendously in this regard. The Tokyo 2020 Games became the first Olympic Games to be migrated into the cloud. Alibaba and Deloitte worked with the Olympic Broadcasting Services (OBS) and media rights-holders (MRH) to implement a new practice: OBS Live Cloud¹².

By Paris 2024, two-thirds of booked remote services across 54 broadcasters were onboarded to OBS Live Cloud, which included 379 high-definition live video feeds and 100 audio feeds. Once these recordings were centralized on the cloud, AI could be put to work and was proven to be more than helpful in multiple aspects, including 360 instantaneous rendition and automatic editing into customized versions. The CEO of OBS, Yiannis Exarchos, praised the way that technological innovations push “the way we convey the stories of athletes, sports”¹³. First, AI could provide a high-resolution 360 digital rendition of significant game moments.

Omega deployed a range of cutting-edge digital equipment, including the reliable Scan’O’Vision MYRIA photo finish camera¹⁴, which can take 10,000 digital images per second on the finish line of races to help capture significant game moments. Thanks to AI and its almost instantaneous reconstruction of a 360 rendition of the moment, we got to enjoy the slow-motion, frame-by-frame replay of the 100-meter men’s final, when American athletes Noah Lyles and Fred Kerley and Jamaica’s Kishane Thompson crossed the finishing line nearly at the same time, according to the naked eye.

Based on these successes, the International Tennis Federation is currently considering broadly applying this technology in its future operations for refereeing the challenges and results.

Second, an AI editor like the one deployed by Alibaba¹⁵ can edit the multiple camera recordings almost instantaneously and export the short videos into customized versions. In previous games, editors needed to take minutes or even hours to edit critical moves at high quality for replay and analysis. Now, the Alibaba AI editor can slice and dice the full length recording into various short segments and tag each with highlighted information automatically. With the help of facial recognition and object tracking algorithms, the AI editor can pick the right recordings to feature a certain athlete or highlight the key play.

When digital technology, including GenAI, is guided well, it goes well beyond generating quantity. Like the Olympics organizing committee, managers can think about integrating AI into their work flow to significantly enhance value creation by improving the quality of the content generation, user engagement, and operations.

Drive Added Value through Mass Personalization

Young generations engage with the Olympics much beyond traditional onsite and online live viewing¹⁶. They want to interact with the games, the athletes, and with each other in real time across various social media platforms. According to unreleased official data, the Paris 2024 Olympics was the first Games where online streaming and viewing through digital media surpassed traditional TV viewers. This presents a significant challenge for the highly complex Olympics.

Without inflating the onsite and remote staff, the International Olympic Committee and the WOP looked to digital technologies for assistance. Deloitte, a long-time WOP, has invested heavily in AI and innovations. In addition to assisting athletes and coaches with personalized training videos and data, the fan data platform¹⁷ is another demonstration of the digital value added to user engagements through mass personalization. The data platform collects behaviour data of Olympics super-fans.

At the same time, the GenAI algorithm can simultaneously translate text content into multiple languages, tag the multiple video segments in local languages and, more importantly, combine with Deloitte’s fan data to help MRHs select, edit, and distribute customized content for each country based on their team’s sport categories and the audience popularity. For example, the same women’s singles badminton gold medal match will be broadcast in China by CCTV in Chinese with tailored replay for He Bingjiao, and in Korea by KBS in Korean with tailored content for the gold medallist An Se-young. At the same time, both contents will pay tribute to the Spanish world champion Carolina Marín, who sadly retired from the game due to knee injuries¹⁸.

Integrate Digital with Diversity

One major objective of the  International Olympic  Committee is to leverage AI¹⁹ to ensure equal access to the games and data. For example, Deloitte has built an integrated AI solution²⁰ for coaches and athletes around the world to access all the game videos and data for analysis and training.

After automatically editing out the segments²¹, AI can also edit the same segment into various forms²² that fit the culture, social media channel style, local languages, and preferences. For example, Chinese table tennis fans²³ can celebrate Ma Long’s sixth Olympic Gold Medal²⁴ on the podium through WeChat, Douyin, and Kuaishou. and Brazilian gymnastics fans can celebrate the historical moment in Portuguese on Twitter, Instagram, and Tiktok, when Rebeca Andrade won gold²⁵ in the women’s floor final and created a first all-black Olympic gymnastics podium²⁶ with her teammate Jordan Chiles and the US superstar Simone Biles. The mass personalization can also connect individual fans who love the same sport or spirit, even if they come from different cultures and backgrounds. On social media X, even former US first lady Michelle Obama saluted “this beautiful moment of sisterhood and sportsmanship”²⁷.

Besides producing and distributing diverse content from billions of new materials, we also saw AI play a role in rejuvenating the Olympics’ archive. To support gender parity of the Paris 2024 Olympics, one of the WOP partners, Alibaba, dug into the archives and created a video “To The Greatness of Her”²⁸ with AI-recolored still images and reconstructed video recordings. These efforts brought the audience back to those historical Olympic moments and connected with the heroines in the video.

Recently, GenAI has been widely used to improve productivity and creativity²⁹ in generating documents, images, and videos. So far, these require heavy human intervention through prompt engineering and generate much waste. The GenAI algorithm developed for the Olympics Broadcasting Services is fully automated. It can handle large amounts of inputs, in thousands of hours of full-length videos from multiple cameras, and turn them into precisely targeted highlights.

Post-Olympics, these technologies could be applied by various brands to connect them with their own customer databases. This will help brands, retailers, and e-commerce to better engage with their customers with personalized content and promotions³⁰. For example, some of the GenAI algorithms are already in use within Alibaba’s e-commerce platforms³¹ such as Taobao, TMall, Lazada, and AliExpress, and have generated additional orders³². For managers interested in improving their customer experience, the insights gathered from the consumption data of these personalized contents can further inform their practices around research, design, development, and production, and benefit the entire upstream supply chain.

About the Author

Chengyi Lin is Affiliate Professor of Strategy at INSEAD and a leading expert on digital transformation and sustainability transition. His research focus on strategic impacts of technologies (e.g. GenAI, renewable energies) and effective organisational changes under uncertainty. Professor Lin serves as board member, CEO advisor, and consultant for multi-nationals and start-ups.

Reference:

1. Olympics President: Paris 2024 on track to reach ‘more than half the world’s population’. 07 August 2024. 365. https://www.ibc.org/news/olympics-president-paris-2024-on-track-to-reach-more-than-half-the-worlds-population/11205.article

2. Olympic Agenda 2020+5. 2020. International Olympics Committee. https://olympics.com/ioc/olympic-agenda-2020-plus-5

3. Meta’s Facebook, Instagram back up after global outage. 06 March 2024. Reuters. https://www.reuters.com/technology/metas-facebook-instagram-down-thousands-downdetector-shows-2024-03-05/

4. Microsoft says 8.5M Windows devices were affected by CrowdStrike outage. 20 July 2024. Tech Crunch. https://techcrunch.com/2024/07/20/microsoft-says-8-5m-windows-devices-were-affected-by-crowdstrike-outage/

5. S. Runner Noah Lyles Wins 100 Meter Olympic Gold—By Just 0.005 Seconds. 04 August 2024. Forbes. https://www.forbes.com/sites/mollybohannon/2024/08/04/us-runner-noah-lyles-wins-100-meter-olympic-gold-by-just-0005-seconds/

6. The Enterprise Multi-Active Disaster Recovery System: Construction Ideas and Best Practices in the Cloud-Native Era. 16 December 2021. Alibaba Cloud. https://www.alibabacom/blog/the-enterprise-multi-active-disaster-recovery-system-construction-ideas-and-best-practices-in-the-cloud-native-era_598361

7. Atos supporting athletes and technological innovation on road to Paris 2024. 21 July 2023. International Olympics Committee. https://olympics.com/ioc/news/atos-supporting-athletes-and-technological-innovation-on-road-to-paris-2024

8. Digital Twins Platform Simplifies Venue Planning. Intel. https://www.intel.com/content/www/us/en/customer-spotlight/stories/digital-twinning-olympics-customer-story.html

9. Paris Olympics 2024. International Olympics Committee. https://olympics.com/en/paris-2024

10. BBC staff for Rio 2016 Olympics to be 40% down on 2012 Games. 07 April 2016. The Guardian. https://www.theguardian.com/media/2016/apr/07/bbc-staff-rio-2016-olympics-2012-games-nbc

11. BBC staff for Rio 2016 Olympics to be 40% down on 2012 Games. 07 April 2016. The Guardian. https://www.theguardian.com/media/2016/apr/07/bbc-staff-rio-2016-olympics-2012-games-nbc

12. Alibaba, OBS partner on AI-fueled OBS Cloud 3.0 for Paris 2024. 26 July 2024. TVB Europe. https://www.tvbeurope.com/media-management/alibaba-obs-partner-on-ai-fueled-obs-cloud-3-0-for-paris-2024

13. IOC President praises broadcast operations as Paris 2024 reaches record audiences. 04 August 2024. International Olympics Committee. https://olympics.com/ioc/news/ioc-president-praises-broadcast-operations-as-paris-2024-reaches-record-audiences

14. OMEGA brings its cutting-edge technology to Gangwon 2024 as Official Timekeeper. 23 January 2024. International Olympics Committee. https://olympics.com/ioc/news/omega-brings-its-cutting-edge-technology-to-gangwon-2024-as-official-timekeeper

15. Alibaba Releases New AI Video Editor ‘Aliwood’. 27 April 2018. Alizila. https://www.alizila.com/alibaba-releases-new-ai-video-editor-aliwood/

16. The Olympic change: How young viewers (and athletes) made Olympic media evolve. Anything is Possible. https://aip.media/blog/young-viewers-changing-olympic-media/

17. Power Behind Paris 2024: Deloitte Pushes Olympics Innovation. 05 July 2024. Technology Magazine. https://technologymagazine.com/articles/power-behind-paris-2024-deloitte-pushes-olympics-innovation

18. Paris 2024: Heartbreak for Spain’s Carolina Marín as badminton star faces devastation. 04 August 2024. International Olympics Committee. https://olympics.com/en/news/paris-2024-devastation-spain-badminton-star-carolina-marin

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21. Video AI: Next-Generation Intelligent Video Production. 27 November 2018. Alibaba Cloud. https://www.alibabacloud.com/blog/video-ai-next-generation-intelligent-video-production_594220

22. AI at the Olympics. Deloitte. https://www2.deloitte.com/us/en/pages/consulting/articles/ai-and-the-olympics.html

23. Tiktok. https://www.tiktok.com/@zhongguoqingnianbao/video/7401502332749368581

24. Tiktok. https://www.tiktok.com/@tabletennis_malong35/video/7401454403363884296?is_from_webapp=1

25. Tiktok. https://www.tiktok.com/@editss2093/video/7399675990747761925

26. Simone Biles on first all-Black Olympic gymnastics podium: “We knew the impact it would make.” – Exclusive. 04 August 2024. International Olympics Committee. https://olympics.com/en/news/simone-biles-on-first-all-black-olympic-gymnastics-podium-we-knew-the-impact-it-would-make-exclusive

27. I’m still not over this beautiful moment of sisterhood and sportsmanship!. 06 August 2024. Twitter. https://x.com/MichelleObama/status/1820812676819190068?t=uQKF8HeexpsVdQASg561Mw&s=19

28. To the Greatness of HER. August 2024. https://www.youtube.com/watch?v=Aso1wqRN5Io

29. How Generative AI Can Augment Human Creativity. 09 August 2023. Harvard Business Review. https://hbr.org/2023/07/how-generative-ai-can-augment-human-creativity

30. The Pragmatist’s Guide to GenAI in E-Commerce. 14 June 2024. BCG. https://www.bcg.com/publications/2024/pragmatists-guide-to-genai-in-ecommerce

31. Alibaba bets on gen AI tools for overseas merchants, executive says. 10 July 2024. Reuters. https://www.reuters.com/technology/artificial-intelligence/alibaba-bets-gen-ai-tools-overseas-merchants-executive-says-2024-07-09/

32. Alibaba: Generative AI Tools Drive 30% Increase in eCommerce Orders. 10 July 2024. PYMNTS. https://www.pymnts.com/news/retail/2024/alibaba-generative-artificial-intellilgence-tools-drive-30percent-increase-ecommerce-orders/

The post How Digital Technologies Drove Values for the Olympic Games appeared first on The European Business Review.

Is your data team stuck in the cycle of managing a patchwork of in-house integrations and point solutions just to get data and insights to decision-makers? As enterprise tech stacks grow more complex, organizations are juggling a mix of legacy on-premises systems and cloud applications, creating an urgent need for seamless integration across disparate systems.

At Foundations 2024, CData is hosting the Enterprise Data Strategy track—a dedicated space for exploring how data leaders can break free from information silos for faster data analysis and insights. Setting a connectivity standard streamlines data access and analysis and speeds time-to-insight across your organization. Join business intelligence, IT, and data architecture leaders as they share their successes in building an interconnected tech stack that drives measurable results.

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How Bayer Achieves Systems Interoperability and Healthcare Regulatory Compliance

Explore Bayer’s journey to modernizing its pharmacovigilance operations and achieve seamless systems interoperability and healthcare compliance. Peter Wilke, IT Solutions Architect at Bayer, shares how automating data processes—like adverse event routing and report submissions—reduced manual engineering efforts, cut operating costs, and streamlined communication with UK health authorities. Discover how these innovations have empowered Bayer to maintain compliance and safeguard revenue streams across Europe. Don’t miss this opportunity to see the role CData plays in Bayer’s transformation.

How a Biotech Manufacturer’s Data Team Automated 80% of their ETL pipelines

The business intelligence (BI) developers at Repligen once relied on homegrown, code-driven ETL processes to manage data warehousing. Today, 80% of their Snowflake warehousing is fully automated with CData Sync—saving their development team an entire year’s worth of effort. Join Repligen’s IT director, Martin Petder, and BI manager, Shawn McNamee, as they share how automation transformed their data processes and freed up resources for high-impact work.

Unanet’s Journey: Moving from Manual Data Handling to Automated, Real-Time Cloud-Based Operations

Solid data management and governance are critical for Unanet, where customers depend on secure, rapid data handling without risk of exposure. As their customer base and data volumes expanded, manually managing these processes grew increasingly complex. In this Fireside Chat, join Assad Jarrahian, Chief Product Officer at Unanet, and Tammie Coles, Head of Sales for CData Virtuality, as they discuss Unanet’s journey in transforming data operations. Together, they’ll explore the shift from manual, on-premises processes to an automated, real-time cloud-based solution that ensures both data governance and scalability for future growth.

B2B Data Architecture to Accelerate Revenue Growth: A Case Study with Healthsource Distributors

Hear from Eric Buxton, Lead Application Developer, and Robb Miller, Director of IT, as they explain how Healthsource’s scalable B2B automations drive customer satisfaction and unlock new revenue streams to give the company a competitive edge in pharmaceutical distribution. Gain practical insights and proven strategies for automating supply chain processes that deliver results fast—this session is a must-see for IT leaders ready to fuel growth and streamline operations.

Pioneering AI in Healthcare: Reducing Mortality and Improving Outcomes at Unity Health Toronto

Unity Health Toronto is pioneering the use of AI to transform patient care, developing groundbreaking solutions designed to reduce mortality, improve hospital efficiency, and enhance patient outcomes. Leading these advancements is Dr. Muhammad Mamdani, VP of Data Science and Advanced Analytics at Unity Health Toronto, who will explain how innovations—like synthesizing complex patient data into organized, actionable timelines, and continually refining AI models to increase precision and accuracy—have saved lives. This fascinating session is for anyone interested in the future of AI-driven healthcare and its far-reaching potential in other industries.

Implementing Scalable ERP Integrations with SrinSoft 

Join Madan Jayagopal, Senior Business Development Manager at Srinsoft, as he shares how their 18 years of experience in ERP integration and digital transformation enable businesses to rapidly implement data integrations and accelerate time-to-market. With expertise in platforms like SAP, Dynamics 365, and NetSuite, Srinsoft, in partnership with CData, has helped companies build scalable, flexible data architectures tailored to their growth and operational needs.

Be part of Foundations 2024 and start building a more connected, agile data strategy

Join us to learn from data experts how to transform your organization’s data—making it more accessible, trustworthy, and actionable for confident decision-making. Discover the tools, knowledge, and strategies that will move your data strategy forward. Register today and set your organization on a path to faster, more accurate intelligence to maintain your competitive edge.

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About the Author

Arun Hari Anand is a Product Marketing Manager at CData Software. Over the past five years, he has had the wonderful opportunity to advance from solutions engineer and solutions engineering manager to his current role of Product Marketer at the company. As a graduate from Dartmouth College in computer science and economics, he is passionate about merging technical and business insights. He thrives on customer interactions, exploring the data landscape, and enhancing the customer experience!

The post Build a Top-Down Connectivity Standard: Inside the Enterprise Data Strategy Track at Foundations 2024 appeared first on The European Business Review.

In an uncertain economy, business leaders need to learn how to harness advanced technologies to remain competitive. Advanced analytics can provide valuable insights into the internal and external forces that affect business operations, allowing leaders to make better decisions that lead to enduring business success. 

The future is unknown and unknowable — this is always true. Yet, by harnessing data, businesses can peer into the murky haze of the coming weeks and months to discern something eerily close to the truth. 

The potentially prophetic power of data is particularly potent in times of economic uncertainty, which is to say, right now. Economists around the world are divided on exactly what path the currently unstable economy will take; some are convinced that the economy will continue to weaken, while others predict stability or even strengthening. For businesses to weather the oncoming storm of unknown economic conditions, they need to deploy effective data strategies, ideally by utilizing the tools and techniques of advanced analytics. 

Advanced analytics refers to a variety of data analysis techniques used primarily to help businesses predict the future and make better decisions, which can be essential for success during times of economic turbulence. Businesses across industries can benefit from advanced analytics, but only if leaders have a firm grasp on what advanced analytics is and how to use it to the greatest advantage. 

What Is Predictive Analytics? 

Arguably the most important field within advanced analytics, predictive analytics uses historical data to forecast future outcomes. Predictive analytics isn’t necessarily new; those well-versed in statistical modeling have been manually conducting predictive analysis for decades, even centuries. However, with the advent of big data and the application of advanced technological tools, like machine learning algorithms, predictive analytics has become remarkably accurate, allowing for sharp strategic decision-making. 

The most common tool in predictive analysis is regression analysis, which helps data analysts (and business leaders) determine the relationships between variables. Using these relationships, data scientists can recognize historical trends and forecast those trends into the future. There are different types of trend forecasting that can be valuable to business leaders, regardless of the prevailing behavior of the economy. 

Types of Trend Forecasting 

Predictive analytics can have dozens of business applications. Some businesses use predictive analytics to foresee machine malfunction, allowing for the development of more effective machine maintenance schedules that prevent unnecessary and expensive downtime. Other businesses employ predictive analytics modeling to determine staffing needs.  

In times of economic uncertainty, any additional information that can aid in decision-making and planning can be valuable. However, there are a few types of trend forecasting that can be of particular value to businesses looking to maintain stability and competitiveness in the coming months. 

Short-term vs. Long-term Forecasting 

Short-term forecasting focuses on the next three to 12 months, which can help business leaders respond quickly to imminent changes affecting business success. Long-term forecasting makes projections between one and four years into the future, which helps business leaders make broader plans for business growth. 

Financial Forecasting 

Big data is transforming financial practices in various ways, from helping leaders assess lending options to enhancing financial services. In financial forecasting, businesses are predicting various factors associated with monetary performance. Financial models might calculate market share, stock market trends, individual product sales, and more. These can be essential in helping business leaders develop budgets and make other critical financial decisions. 

External Macro Forecasting 

Various economic factors can impact individual businesses in various ways. External macro forecasting strives to determine how a business will be affected by broad economic trends, like inflation, tax rates, labor supply, government activities, and more. This type of trend forecasting can give business leaders some direction in goal setting, especially if a business is planning to expand in the near future. 

Internal Forecasting 

Internal business forecasts review a business’s operations, revealing details of its internal capacity, such as limitations that may slow growth or opportunities for greater investment of resources. Because business leaders have more control over their internal processes than external economic factors, having insight into the future of the business’s internal workings is exceedingly valuable for creating stability and success when the economy is unreliable. 

Customer Behavior Forecasting 

When a business knows what kind of consumer behavior to expect, leaders can develop marketing and sales strategies to capitalize on that behavior, which ultimately results in higher profits and better financial stability. Advanced analytics that helps reveal customer behavior are exceedingly valuable when the economy is volatile because continuing to connect with customers is the only way a business will survive troubled economic times. 

Demand Forecasting 

Arguably the largest field within trend forecasting, demand forecasting helps businesses understand the future demand for their products or services. Even when the economy is stable, demand forecasting can be useful in helping businesses weather seasonal highs and lows, but when the economic forecast is uncertain, long-term demand projections can help businesses develop realistic expectations for sales in the coming months and years. There are two primary types of demand forecasting: 

• Passive demand forecasting, which utilizes past sales data to predict demand. This method is only reliable if a business’s sales history is stable and if the economic activity of the previous time reflects that of the future. 
• Active demand forecasting utilizes market research, economic outlook, growth projections, and other external factors to predict demand. This method works better for new businesses that may lack historical sales data and for businesses that need to determine how new external factors will impact demand. 

How to Harness Advanced Analytics 

Small and medium-sized businesses new to advanced data analysis have access to various tools to aid in the integration of predictive analytics into the decision-making process. In fact, during times of economic uncertainty, adopting technological advancements like software and AI can be essential to maintaining efficiency across business processes. In terms of big data analytics for trend forecasting, there is no more valuable technological tool than artificial intelligence. 

In the past, data scientists may have used all manner of manual methods and models for collecting and crunching data to reveal insights. Market research, sales force composites, expert opinions, and more may have contributed a picture to aid business leaders in decision-making. However, the application of machine learning, generative AI, and other AI solutions eliminates a significant amount of the guesswork that once went into forecasting, helping both data scientists and business leaders access more accurate predictions for the future. 

Businesses may invest in any of the dozens of AI-backed predictive analysis platforms, some from big names in computing like IBM or Microsoft, or they may conscript data analysis firms to build a bespoke solution that suits their unique needs. In any case, AI is undoubtedly the future of predictive analytics, and investing in an AI-driven tool today will likely provide the insights businesses need to maintain stability into the future. 

Predicting, Planning, Protecting 

Though the future will always be shrouded in mystery, businesses can tease out some of its simpler secrets with the help of advanced analytics. By using AI to analyze big data, business leaders can peer into the time ahead to see the trends that are most likely to have a great impact on their business, which means they can make decisions that allow their business to survive and thrive. 

“Uncertainty” is one of the scariest words to business leaders, and uncertain economic activity poses a real threat to business security. Fortunately, with advanced analytical tools and techniques, business leaders can achieve the stability they need to remain competitive, regardless of whether the economy crashes or soars.

About the Author

Indiana Lee is a writer, reader, and jigsaw puzzle enthusiast from the Pacific Northwest. An expert on business operations, leadership, marketing, and lifestyle.

The post Predict, Plan, and Protect: Leveraging Advanced Analytics to Navigate Economic Uncertainty  appeared first on The European Business Review.

Facilities management software is at the forefront of transforming how businesses operate and maintain their environments. As technology advances, these platforms must innovate to stay relevant and effective. Explore the upcoming trends, technological integrations, and key improvements poised to redefine facilities management over the next five years, driving efficiency and sustainability in our exclusive interview with Philip Myers of mpro5. 

What emerging trends do you see shaping the future of facilities management software platforms over the next five years? 

I don’t foresee any major new trends – I think the industry has been grappling with the same issues for the last few years and we are still seeing that with clients operating in the space: 

• Paperless FM – many businesses still rely on paper forms to verify jobs have been done and provide an audit trail if needed. Not only does this use unnecessary amounts of paper, but it is also vulnerable to exploitation, human error, loss, and damage. Digitising these processes provides an infallible audit trail, time stamps, and reports problems automatically, giving real time insight to businesses so they can address any issues instantly. 
• Energy conservation – energy-saving is becoming ever more critical, and FM software is perfectly placed to monitor building usage and performance. Monitoring everything from lighting, HVAC, and air quality to room usage and cleaning schedules, can reduce unnecessary power consumption, resource usage, and waste. 
• Perfecting hybrid work environments – many businesses and buildings are still perfecting the balance of operating hybrid working models. Again, FM software is well placed to help monitor building usage to optimise maintenance, security, and cleaning schedules, and even office opening patterns to streamline energy and resource usage and adapt them to hybrid working patterns. 
• Embracing IoT – most companies are still to fully unlock the power of IoT to improve their FM processes. Cameras and various sensors (temperature, door opening, occupancy) can be used to trigger both preventative and remedial actions, but all are still far too underutilised across the industry. More on that shortly… 

How do you envision the integration of technologies like IoT, AI, and machine learning impacting the capabilities of facilities management software?  

More and more advancements in machine learning and AI will make platforms smarter around prioritising problems and issues to allocate limited resources more efficiently. We will also see these platforms begin to propose remedial actions based on past process completion. For example, if a common problem occurs with an HVAC system, the software will suggest actions to rectify the problem and what parts might be needed, or additional actions or expertise are required, to fix it.  

Though the term Internet of Things (IoT) was first used way back in 1999, the power of cameras and sensors is still not being fully exploited in facilities management. Granted, the practical applications of these technologies have only been a reality in the last decade or so, but there is still so much more FM professionals could use them for. For example, using cameras to retrospectively investigate thefts is far less effective than using cameras to alert a security guard to an intruder’s presence as it’s happening, who can respond in real-time to the intruder and stop the theft. Responding to a fire door being left open instantly due to a sensor, instead of noticing it potentially hours later on a scheduled patrol, could prevent a multitude of unwanted scenarios unfolding. Similarly with temperature monitoring on fridges and freezers – fix them (or simply close the door!) before food spoils and goes to waste. Prevention is always easier and cheaper than cure, and IoT can carry the weight of this challenge for many businesses.  

What improvements do you think are necessary to enhance the user experience for facilities management software platforms? 

They must be quick, simple, and easy to use. Being mobile-friendly is a non-negotiable. Clients should also have the choice to implement them on devices they supply, or on employees’ own devices by adopting a bring your own device (BYOD) policy, which has multiple benefits around cost savings and device familiarity for users. They need to operate on limited or zero connectivity so that users never have to wave devices around to get a signal! You may not be surprised to hear that mpro5 offers all of the above.  

In addition, and something we are always working on, is the use of AI and machine-learning to remove the burden from users, streamline processes, reduce repetitive tasks and ease adoption to increase user efficiency and productivity.

How can facilities management software contribute to sustainability and energy efficiency goals for businesses? 

I touched on this earlier, but it’s all about reducing consumption and waste by responding to incidents in a timely and efficient manner. Cleaning restrooms based on usage and footfall is far more efficient than doing it by schedule. Using sensors on doors and windows will prevent heat loss during winter and optimise HVAC performance year round, by making sure they are not left open unnecessarily. Sensors on fridges and freezers can alert maintenance teams to issues before food spoils and is wasted. Simply digitising offline audits and logbooks can save hundreds of thousands of pounds in paper and printing alone. One of our clients saves over £1million a year in paper and printing costs since switching to mpro5. 

With the increasing reliance on digital platforms, what measures should be prioritised to ensure data security and privacy in facilities management software? 

As with all technology, implementing robust security measures is a must. Regular audits and penetration testing is critical, as is employee cybersecurity training. Suppliers should also invest in the latest security technologies and always keep up to date. We of course adopt these practices and we also use end-to-end data encryption at rest and in transit, and are ISO27001 certified.  

How important is customisation and scalability in facilities management software, and what are the challenges in providing these features? 

This is a challenge faced by most software companies and a decision that needs to be made by the business. I generally see three options: 1. You build something once and sell it to many, adopting a lower value, higher volume approach to sales. 2. You go the other way, embrace customisation, and make bespoke solutions that are more expensive and time and resource intensive, where you have fewer clients that are of higher value. Or 3., you diversify your offering – make different versions, tiers, or instances of a similar product and price them accordingly. It all comes down to the strategic direction and desires of the business. 

Regardless of your approach, any software needs to be flexible to adapt to evolving, changing industry trends, and the capability to add new features and integrate with new technologies is a must. Without the ability to innovate, embrace change, and adopt new technologies, your offering will never get off the ground. 

Executive Profile

Philip Meyers joined Crimson Tide in August 2023 as COO, bringing a wealth of experience in IoT and process management, and was promoted to CEO in April 2024. His previous experience includes Vice President of Capabilities and Innovation at Inmarsat Global, the world leader in global mobile satellite communications, senior positions in smaller satellite businesses, and Channel Sales Manager for BlackBerry.  

The post Sustainable Solutions for Businesses: Enhancing Efficiency with Facilities Management Software appeared first on The European Business Review.

What key trends do you see shaping the future of data analytics in business intelligence?

In a little more than a decade, data analytics went through several big transformations. First, it became digitised. Second, we witnessed the emergence of “big data” analytics, driven partly by digitisation and partly by massively improving storage and processing capabilities. Finally, in the last couple of years, analytics has been transformed once again by emerging generative AI models that can analyse data at a previously unseen scale and speed. GenAI is becoming a data analyst’s personal assistant, taking over less exciting tasks, from basic code generation to data visualisation.

I believe the key effect of generative AI – and the main future trend for data analytics – is data democratisation. Recently, there’s been a lot of activity around “text to SQL” products to run queries in natural language, meaning that people without specialisation in data sciences get the possibility to dive deeper into data analysis.

However, we shouldn’t get carried away with the hype too quickly. Those AI-powered tools are neither 100 per cent accurate nor error-free, and noticing errors is more difficult for less-experienced users. The holy grail of analytics is precision combined with a nuanced understanding of the business landscape – skills that are impossible to automate unless we reach some sort of a “general” AI.

The second trend that is critical for business data professionals is moving towards a single umbrella-like AI system capable of integrating sales, employee, finance, and product analytics into a single solution. It could bring immense business value due to cost savings (ditching separate software) and also help with the data democratisation efforts.

Can you elaborate on the role of machine learning and AI in next-generation data analytics for businesses? 

Generative AI somehow drew an artificial arbitrary line between next-gen analytics (powered by GenAI) and “legacy” AI systems (anything that came before GenAI). In the public discourse around AI, people often miss the fact that the “traditional” AI isn’t an outdated legacy; GenAI is intelligent only on the surface, and the two fields are actually complementary.

In my previous answer, I highlighted the main challenges of using generative AI models for business data analytics. GenAI isn’t, strictly speaking, intelligence; it is a stochastic technology functioning on statistical probability, which is its ultimate limitation.

GenAI isn’t, strictly speaking, intelligence; it is a stochastic technology functioning on statistical probability, which is its ultimate limitation.

Increased data availability and innovative data scraping solutions were the main drivers behind the GenAI “revolution”; however, further progress can’t be achieved by simply pouring in more data and computational power. Moving towards a “general” artificial intelligence, developers will have to reconsider what “intelligence” and “reasoning” mean. Before this happens, there’s little possibility that generative models will bring to data analytics something more substantial than they have already done.

Saying this, I don’t mean there are no methods to improve generative AI accuracy and make it better at domain-specific tasks. A number of applications already do it. For example, guardrails sit between an LLM and users, ensuring that the model provides outputs that follow the organisation’s rules, while retrieval augmented generation (RAG) is increasingly employed as an alternative to LLM fine-tuning. RAG is based on a set of technologies, such as vector databases (think Pinecone, Weaviate, Qdrant, etc.), frameworks (LlamaIndex, LangChain, Chroma), and semantic analysis and similarity search tools.

How can businesses effectively harness big data to gain actionable insights and drive strategic decisions? 

In today’s globalised digital economy, businesses don’t have a choice of avoiding data-driven decisions, unless they operate in a very confined local market and are of limited size. To drive competitiveness, an increasing number of businesses are collecting not only consumer data that they can get from their owned channels but also publicly available information from the web for price intelligence, market research, competitor analysis, cybersecurity, and other purposes.

Up to a point, businesses might try to get away without using data-backed decisions; however, when the pace of growth increases, companies that rely only on gut feeling unavoidably start lagging behind. Unfortunately, there are no universal approaches to harnessing data effectively that would suit all companies. Any business has to start from the basics: first, define the business problem; second, answer, very specifically, what kind of data might help to solve it. Over 75 per cent of data that businesses collect ends up as “dark data”. Thus, deciding what data you don’t need is no less important than deciding what data you do need.

In what ways do you envision data visualisation evolving in the context of business intelligence and analytics?  

Most data visualisation solutions today have AI-powered functionalities that provide users with a more dynamic view and enhanced accuracy. Further, AI-driven automation also allows businesses to analyse patterns and generate insights from larger and more complex datasets while freeing analysts from mundane visualisation tasks.

I believe that data visualisation solutions will have to evolve towards more democratic and noob-friendly alternatives, bringing data insights beyond data teams and into sales, marketing, product, and client support departments. It is hard to tell, unfortunately, when we could expect such tools to arrive. Up until now, the focus of the industry hasn’t been on finding the single best visualisation solution. There are many different tools available on the market, and they all have their advantages and disadvantages.

Could you discuss the importance of data privacy and security in the era of advanced analytics, and how businesses can ensure compliance while leveraging data effectively?  

Data privacy and security were no less important before the era of advanced analytics. However, the increased scale and complexity of data collection and processing activities also increased the risks related to data mismanagement and sensitive-data leaks. Today, the importance of proper data governance cannot be overstated; mistakes can lead to financial penalties, legal liability, reputational damage, and consumer distrust.

In some cases, companies deliberately cut corners in order to cut costs or gain other business benefits, resulting in data mismanagement. In many cases, however, improper data conduct is unintentional.

Let’s take an example of GenAI developers, who need massive amounts of multifaceted data to train and test ML models. When collecting data at such a scale, it is easy for a company to miss that parts of these datasets contain personal data or copyrighted material that the company wasn’t authorised to collect and process. Even worse, getting consent from thousands of internet users who might be technically regarded as “copyright” owners is virtually impossible.

So, how can businesses ensure compliance? Again, it depends on the context, such as the company’s country of origin. The US, UK, and EU data regimes are quite different, with the EU having the most stringent. The newly released EU AI Act will definitely have an additional effect on data governance, as it tackles both developers and deployers of AI systems within the EU. Although generative models fall in the low-risk zone, in certain cases they might still be subject to transparency requirements, obliging developers to reveal the sources of data the AI systems have been trained on, as well as data management procedures.

However, there are basic principles that apply to any company. First, companies must thoroughly evaluate the nature of the data they are planning to fetch. Second, more data doesn’t equal better data. Deciding which data brings added value for the business and omitting data that is excessive or unnecessary is the first step towards better compliance and fewer data management risks.

How can businesses foster a culture of data-driven decision-making throughout their organisations? 

The first step is, of course, laying down the data foundation – building the customer data platform (CDP), which integrates structured and cleaned data from various sources that the company uses. To be successful, such a platform must include no-code access to data for non-technical stakeholders, and this isn’t an easy task to achieve.

GenAI isn’t, strictly speaking, intelligence; it is a stochastic technology functioning on statistical probability, which is its ultimate limitation.

No-code access means that the chosen platform (or “solution”) must hold both an SQL interface for experienced data users and some sort of “drag and drop” function for beginners. At Oxylabs, we chose Apache Superset to advance our self-service analytics. However, there is no solution that would fit any company, with only pros and no cons. Moreover, these solutions require well-documented data modelling.

When you have the necessary applications in place, the second big challenge is building the data literacy and confidence of non-technical users. It requires proper training to ensure that employees handle data, interpret it, and draw insights correctly. Why is this a challenge? Because it is a slow process, and it will take time away from the data teams.

Fostering a data-driven culture isn’t a one-off project. To turn data into action, you will need a culture shift inside the organisation, as well as constant monitoring and refinement efforts to ensure that non-technical employees feel confident about deploying data in everyday decisions. Management support and well-established cooperation between teams are key to making self-service analytics (or “data democratisation”, as it is often called) work for your company.

Executive Profile

Rytis Ulys holds over eight years of experience in various analytical and consulting roles in both start-up businesses and enterprise-grade organisations. Currently, he is leading a team of seven data professionals at Oxylabs, a market-leading web intelligence acquisition platform. Rytis managed to build one of the company’s core teams from scratch in just two years. As a thought leader, he covers topics ranging from data architecture and data engineering to advanced data modelling. 

The post Data Analytics: The Gut Feeling is no Longer Enough – Interview with Rytis Ulys of Oxylabs appeared first on The European Business Review.

Real-Time Stock Data

One of the key advantages of using FMP’s APIs is access to real-time stock data. This feature allows users to stay on top of market movements and make split-second decisions. In a world where milliseconds can make the difference between profit and loss, having access to the most up-to-date information is crucial. FMP’s real-time stock data API provides data on over 70,000 symbols, including stocks, ETFs, mutual funds, indices, and more.

Historical Stock Data

For those interested in historical analysis, FMP’s historical stock data API is an invaluable resource. It provides daily, hourly, and even minute-by-minute historical data going back several decades. This wealth of historical information allows analysts to identify long-term trends, backtest trading strategies, and perform in-depth technical analysis. The ability to access such comprehensive historical data through a single API streamlines the research process and enables more sophisticated analysis.

Fundamental Analysis

Fundamental analysis is another area where FMP’s APIs shine. Their company financial data API offers in-depth insights into corporate financials, including balance sheets, income statements, and cash flow statements. This level of detail is essential for investors looking to evaluate a company’s financial health and potential for growth. The API provides both quarterly and annual financial statements, allowing for both short-term and long-term analysis.

Market Data

Moreover, FMP’s market data API provides a broader view of the financial landscape, offering data on various market indices, sectors, and economic indicators. This comprehensive approach ensures that users have all the necessary information at their fingertips to make well-rounded investment decisions. From GDP and inflation data to sector performance and market sentiment indicators, FMP’s market data API covers all bases.

Commitment to Data Accuracy and Timeliness

One of the standout features of FMP’s offering is their commitment to data accuracy and timeliness. Financial statements are updated in real-time as companies release their reports, and each statement is audited and standardized to ensure consistency and reliability. This attention to detail sets FMP apart from many other financial data API providers and gives users confidence in the data they’re working with.

Developer-Friendly APIs

For developers, FMP’s APIs are designed with ease of use in mind. The APIs use RESTful architecture and return data in JSON format, making them easy to integrate into a wide range of applications. Whether you’re building a trading platform, a portfolio management tool, or a financial analysis application, FMP’s APIs provide the data backbone you need.

The Future of Financial Data APIs

As the financial industry continues to evolve, the importance of reliable and accessible financial data APIs cannot be overstated. The rise of algorithmic trading, robo-advisors, and AI-powered financial analysis tools has created an unprecedented demand for high-quality financial data. FMP is at the forefront of meeting this demand, continually expanding and refining their API offerings to meet the evolving needs of the financial industry.

Innovation in Financial Data

Looking ahead, we can expect to see continued innovation in the field of financial data APIs. As new financial instruments emerge and markets become increasingly interconnected, the need for comprehensive, real-time financial data will only grow. FMP is well-positioned to lead this charge, with their commitment to data accuracy, comprehensive coverage, and user-friendly APIs.

Conclusion

In conclusion, Financial Modeling Prep stands at the forefront of the financial data API revolution, empowering users with the tools they need to navigate the complex world of finance. Whether you’re a professional investor, a fintech developer, or a financial analyst, FMP’s suite of APIs provides the data foundation necessary for success in today’s data-driven financial landscape. For more information and access to comprehensive financial data, visit Financial Modeling Prep.

The post The Power of Financial Data APIs in Modern Finance appeared first on The European Business Review.

As a business owner, you’re constantly thinking about how you can implement new ideas into your strategy to boost your company’s success. Data analytics helps to paint a clear picture of where you stand and where you should make changes. Keep reading to learn 10 tips to unlock business success with advanced data analytic strategies. 

What Are Data Analytics?

Just as there’s been a push for more data collection, there has been a rise in analytic technology. Data analytics takes data that you have collected, whether by hand or through online tools, and analyzes it to make conclusions. 

“If you’re starting a business and want to understand where your success or failures are stemming from, you should start with data analytics,” said Brianna Bitton, Co-Founder of O Positiv, a company that specializes in women’s vitamins. “Analyzing data gives you the ability to see between the lines and understand how you should move forward.”

Data analytics is a system that can provide a variety of outcomes. It is wired to comb through data collection points and can be programmed to look for specific intersections and trends. Data analytics is what all businesses need to reach the next level.

What Can Advanced Data Analytics Do for Your Business?

Data analytics help unearth important truths about your business and its potential. Access to advanced technology has helped businesses to make better decisions when it comes to how they interact with their customers, how they manage their inventory, and how they decide what comes next. 

“The ability to bring data-driven insights into decision-making is extremely powerful, all the more so given all the companies that can’t hire enough people who have these capabilities. It’s the way the world is going,” shared Jan Hammond, Professor of Manufacturing and Senior Associate Dean of Culture and Community at Harvard Business School. 

Whenever you’re making a decision for your business, it can be extremely helpful to use data as the driving force. Data backs up your decision-making and gives you an idea of what is and isn’t working. 

10 Data Analytic Tips To Help Your Business Succeed

Implementing data analytics into your business might seem like a foreign concept, especially if you are not a tech wizard. Luckily, you don’t need to be a data analyst to bring data analytics into your business. 

1. Turn Data Into Insights

If you want your business to succeed, you need to learn how to turn your data collection into actionable insights. Making your data readable and accessible can help people throughout your business understand how to interpret the figures. 

“Data is a collection of information that you organize. Without reading and sorting your data, you’re left looking at a mess of numbers. You have to figure out why the data you’ve collected looks the way it does to understand how you can use it,” explained Titania Jordan, CMO of Bark Technologies, a company that provides phones for kids with built-in safety features.

The insights you gather from your data are what your business will use to plan for its future. They help guide your business to what’s next by offering connections and evidence.

2. Find Data You May Have Missed

The more data that you collect, the more information you’re able to understand. As you dive deeper into your data collection, you find different patterns and interactions. These patterns have always been there, but not to the naked eye. 

“It sometimes takes sifting through hundreds and thousands of data points to find what you’re looking for. Half the time, you may not even be sure what you want to find, and that’s okay. The data will spell it out for you in time,” said Shaunak Amin, CEO and Co-Founder of ByStadium.

Data analytics help to simultaneously narrow and expand the information that you’ve collected. You may find hidden details that are useful to decision-making in your business while also seeing a broader picture that can open you up to new opportunities. 

3. Keep Your Data Current

You never know when the data that you’ve collected is going to become useful. It could take years before your data finally makes sense. Because of this possibility, you should be maintaining your data collection and keeping it organized year over year (YoY).

“Every piece of data that you collect is going to be worthwhile. Your business will change throughout the years, but the variables that you track each year can stay the same. Choosing variables with longevity in mind will help you to see patterns and trends for years to come,” advised Scott Chaverri, CEO of Mito Red Light, a company known for their red light therapy devices.

You want to be able to find data when you need it. By storing it correctly and safely, you can ensure access to it when the time comes. Some data will even be helpful in a multitude of situations. 

4. Improve Your Data Collection

As your business grows, you might find you have the ability to collect data from different sources. This is essential for your business’s growth and development. Your understanding of the data you’ve collected will only grow as you use more diverse sources. 

“Every department under your business could benefit from dedicated data collection sources. Your marketing sources will look different from your inventory sources. All of this data is useful, just in its own way,” explained Andrew Meyer, CEO of Arbor.

Over time, the data that you’ve collected will make more sense to your business. While you might not understand why certain sources are useful now, you can’t predict what you’ll be looking for in the future. Of course, the data might be able to!

5. Create Analytic Products

Analytics projects help us complete tasks and often have a finite use. This can be helpful for businesses in the process of data collection who need to execute a specific plan. However, analytic products work to maximize user value. As a result, an analytic product is adaptable and ever-changing. It works to continue to improve how you view data rather than just bring specific data in. 

“There is more collaboration from a team when an analytic product mindset is involved. You can keep working to fine-tune and expand with an analytical product mindset so that you can improve your customer’s experience with your business,” shared Bob Craycraft, CEO of Cadence Petroleum.

Try adopting an analytic product mindset for your business to keep the focus on the bigger picture: making your business more useful to your customers. 

6. Build Up Your Analytics Team

There are professionals who can make data analytics more accessible to your business, and they should be some of the first people you hire. Data collection should start as soon as you launch a business, but it can be difficult to try to maintain this work without a dedicated team.

“Data analysts exist for a reason. They can use and understand the programs being used to collect and sort data, searching for key trends and figuring out ways to implement changes to your business from what the data tells them,” said Amanda Howland, Co-Founder of ElleVet Sciences, a company that specializes in dog CBD.

Once you find a core team of data analysts, you might begin to feel more at ease about data. You don’t have to be a tech genius to start the data collection and analyzing process — you just need to find one. 

7. Use Key Performance Indicators

While you can’t control where the data will take you, you should still implement quantifiable measures called key performance indicators (KPIs) that help you evaluate your collection. Giving your data structure can help you extract valuable information from it.

“When you start asking the right questions, patterns begin to fall into place. You can look at your data and ask a variety of questions, and the data will give you answers,” explained Bryan Welker, VP of Growth Marketing at L-Nutra, a company known for their proprietary Fasting Mimicking Diet (FMD), Prolon.

Even just one group of data can tell you multiple different outcomes. You can sort through your data using KPIs to better understand these outcomes. 

8. Refine Your Analytics Models

As with everything in the business world, data analytics is constantly evolving. How you collect, view, and interpret data — and how you use these interpretations — will change from situation to situation. 

“When things are changing, your data also changes, and when your data changes, your models change. Analytics models are not a constant entity,” said Prashanth Southekal, head of DBP-Institute. 

You can’t assume that the analytic models that you’re currently using for one data set will work in another data set. Similarly, you can’t assume how you analyzed data five years ago will be the same today. Be prepared to refine your models and let them lead to how you consume data. 

9. Practice Data Storytelling

Once you’ve gotten your data and turned it into insights, you need to know how to share those insights with your team. Practicing data storytelling helps to educate your employees and stakeholders on what the data in front of them means. 

“The more you can explain about your data, the easier it is for people to read it. Showing how two points connect or how one collection informs the next will help to weave a narrative that then can be used to help implement new strategies into your business,” explained Jim Mitchell, Chief Growth Officer of Awesome CX by Transcom, a company that specializes in customer experience solutions.

Storytelling helps to educate in a way that actually reaches your audience. How you explain your data to your marketing strategist might be different than how you might explain it to your business development manager. The key is to have a strong enough understanding of the information in question to tailor your explanations to your audience.

10. Embrace the Data Revolution

You can’t avoid data. Even if you think you’re not actively involved in data collection and analysis, data is constantly being collected in every department of your business. 

“The longer you wait to take data seriously, the more you hurt your business. Once you’ve accepted that data analytics are here to stay, you can come up with ways to help push your business to success with its help,” said Sam Emara, CEO of Foxy AI.

The first step might be hiring a data analyst to help you get the job done right. Then, once you’ve identified your KPIs and envision the future you want for your business, the data collection will come naturally. 

Take Every Opportunity for Success

No matter what kind of business model you’ve adopted for yourself, data can help to better understand how your business is holding up. If you want concrete measures to see your successes or failures, data can provide that for you. Don’t shy away from it now — embrace data analytics and all that it can do for your business or risk being left in the past.

The post 10 Tips To Unlocking Business Success With Advanced Data Analytics appeared first on The European Business Review.

Data centers can emit significant amounts of carbon, so to reduce operating costs and become more sustainable, monitoring and controlling data center power usage is becoming an essential part of operations.

However, monitoring and controlling power usage in data centers is often easier said than done. Thankfully, there are ways you can accomplish both without increasing costs.

Tips on Controlling Data Center Energy Usage

Before you can start controlling how much energy your data center uses, you need to start at the beginning. This means measuring the amount of electricity your data uses in an average day. No, you can’t grab a copy of your latest energy bill. Unfortunately, measuring energy consumption isn’t this easy.

A good place to start is with your PDUs (power distribution units). Switching from traditional outlets to PDUs can be an effective way of reducing energy consumption. The PDU can turn outlets on and off depending on if the plugged-in equipment is being used. Remote power panels, UPS (uninterrupted power supply), and other building meters can provide additional information.

You may also want to invest in energy management software. The software can analyze the data and create a report measuring everything from your energy usage to the data center’s carbon footprint.

Review the Data Center’s Environment

Once you know how much energy your data center is consuming, it’s time to look at the environment. Chances are, the environment is contributing to the data center’s high energy usage rate. Taking a few simple steps can significantly reduce energy consumption, resulting in lower costs and a greener data center:

• Temperature sensors on each rack can monitor cooling. You instantly know if you’re overcooling the stack. Raising the temperature in a data center can result in significant savings.
• Using humidity sensors lets you know when you need to add or remove humidity. This way, your humidifiers and dehumidifiers are constantly switching on and off. The sensors only turn the equipment on when it’s necessary.
• Airflow sensors are another inexpensive investment with a potentially high ROI. Sensors by your hot and cold air returns are an effective and efficient way of monitoring your cooling system. In some data centers, the biggest energy hog is the cooling system. By monitoring its performance you can dramatically reduce energy costs.

You should also plan on measuring air pressure in the data center. For example, partition leaks can increase energy usage. By monitoring air pressure, you can more easily identify potential problems and take the necessary repair steps.

Don’t Keep Your Data Center at Freezing Temperatures

Hopefully, you’re using temperature sensors—this way, you have a good idea of the optimal temperature for your data center. 

If you’re not sure what the optimal temperature is for a data center, you can always refer to the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) guidelines. These guidelines recommend keeping temperatures between 65-80°F. Yes, these temperatures can seem a little high but they can also represent significant savings.

Remember the temperature sensors you installed on the racks? These sensors can alert you if temperatures rise to unsafe levels since this way you can lower the temperature as needed while still taking advantage of the potential savings. 

And yes, this may mean monitoring the temperature a little more closely but the reduced energy costs make it well worth the little extra effort.

Keep Everything Contained

Okay, so you probably already have all of the necessary equipment contained in your data center,his isn’t exactly what containment means in this scenario. You want to keep the cold supply air from the cooling system separate from the IT equipment’s hot exhaust. 

In other words, don’t mix your hot and cold air since all you get is lukewarm air and this does little to keep the equipment at the ASHRAE recommended temperature. Instead, your cooling system is working overtime to counteract the warm air and this is a waste of energy.

You can use doors and ceiling panels to keep the warm air separated until it’s returned to the cooling system. The same strategy in the cold aisle can effectively keep cool air where it’s needed. You can also implement the chimney system, The warm exhaust air is moved up through the chimney to the ceiling before returning to the cooling system. Another option to consider here is using the curtain system.

With the curtain system, your racks are in alternating rows facing front to front and back to back. Curtains keep the cool air away from the warm exhaust. Whichever method you decide to use often depends on the existing configuration of your data center.

Locate Your Ghost Servers

If you don’t have at least a few ghost servers in your data center, you’re doing better than most organizations. Ghost servers are those servers you never use but are still plugged in. Not only is the unused server taking up valuable space, but it’s also using energy.

A good idea is to run a report to see which servers are in use and performing vital functions. For the ones not being used, go ahead and unplug them from the power outlets. You can even go a step further and take the old servers to an electronics recycling facility. 

Now, you’re freeing up space, getting rid of clutter, and doing something beneficial for the environment. You may be amazed at how you can save on energy costs just by unplugging your unused servers.

Controlling Energy Usage Can Have Big Rewards

Getting a handle on your data center’s energy usage can do more than cut your utility costs. You’re also doing something good for the environment by reducing your organization’s carbon footprint. With more consumers searching for eco-friendly businesses, reducing energy consumption is a great place to start. 

Best of all, cutting back on energy consumption is often easier than you may think. Sometimes, all it takes is a few basic steps.

The post Is Your Data Center an Energy Hog? How to Control Energy Usage appeared first on The European Business Review.

But what exactly is a single source of truth, and how do you implement it?

What Is a SSOT?

As the name suggests, a single source of truth is a singular, centralized, accessible location where the “truth” (aka data) within an organization can be stored, organized, and retrieved. The primary purpose of an SSOT is to centralize and streamline a company’s data, and it stands in contrast to data structures that involve multiple sources of truth.

As an example, let’s consider an organization that has 10 different software platforms run by 10 different departments, each of whom is gathering data both independently and on overlapping topics. 

Understandably, this system would be chaotic, with potentially redundant data entries and probable contradictions between systems and departments.

One solution could be implementing an SSOT in this scenario, replacing the 10 different software platforms with a single software platform that all 10 departments use equally. 

Instead of making a redundant entry, a department would see that the entry is already there and valid, preventing time waste. If a contradiction of data arises, it can be resolved within the platform.

Why Is an SSOT Valuable?

Why is a single source of truth so valuable?

• Cross-departmental coordination. Organizations often gather data from multiple sources simultaneously, and in many cases, those sources are related to different departments with different goals. This causes significant chaos and reduces the data analytic potential of the organization. Establishing a single source of truth allows for more convenient coordination and collaboration between departments, reducing silos as well as streamlining data management.
• Centralization and accessibility. This system is also important because it centralizes data and makes it more accessible. Instead of trying to track down a single data point on multiple platforms or dealing with confusion about contradictory entries across platforms, users should know exactly where to go for what they’re trying to find. If the SSOT is intuitive and solid in terms of user experience, the situation will be even better.
• Fewer errors, mistakes, and contradictions. Many companies adopt a single source of truth so there can be fewer errors, mistakes, and contradictions. Tracking data is a good thing, generally, but the truth of this depends on the data being accurate and consistent across the board. Any mistake or contradiction can cost a lot of time and cause analytic headaches, so it pays to prevent these hiccups as much as possible.
• Productivity and speed. An SSOT has the potential to increase productivity and speed across the board. With fewer mistakes, faster data accessibility, more intuitive systems, and more collaboration, it’s only natural that employees are going to get more done in less time.
• Data security. In most cases, a single source of truth also leads to better data security. That’s mostly because it’s easier to protect a single platform than to protect multiple platforms simultaneously. Of course, this is dependent on your ability to select a highly secure platform as your single source of truth.
• Higher transparency. Organizations that use a single source of truth have higher transparency than their counterparts. Transparency leads to higher morale, more public trust, and a host of other benefits.

How to Create a SSOT

If you’re interested in creating an SSOT for your organization, these are the steps you should follow:

• Analyze your existing frameworks. Make sure you spend time analyzing your existing frameworks. What systems do you use? How do you approach and organize data? How do your departments work together? Try to analyze the root causes of your problems and take inventory of all your data sources.
• Consider investing in comprehensive software. Is it possible to replace all these data sources with one piece of comprehensive software? For many enterprises, this is the right move, but it’s not the only option available.
• Assemble integrations. It’s also possible to stitch together a single source of truth by integrating multiple platforms into each other. This does demand more work, and leaves a few problematic gaps, but it could be cheaper and simpler than comprehensive software.
• Create clear protocols. Follow up by creating clear protocols for how data is meant to be gathered and entered into your SSOT. Do whatever is necessary to make sure these protocols are consistently followed.

If you don’t already have an SSOT, now is the time to audit your current approach to data organization and analytics. Introducing and coordinating a single source of truth does demand an investment of both time and money, but it may be worth it to see the impressive benefits at the end of the line.

The post Why You Need to Have a Single Source of Truth (SSOT) appeared first on The European Business Review.

What is Vector Data?

To grasp the concept of vector data, imagine it as a unique fingerprint of data. Just as a fingerprint contains complex patterns that identify an individual, vector data encodes the complex characteristics of information into a simplified, yet highly descriptive format. This makes it ideal for representing data in fields like machine learning and multimedia retrieval.

Vector Data vs. Scalar Data

Vector data, therefore, is not just about size or volume but about the depth and multidimensionality that allows for richer data representation and processing.

The Need for a Vector Database

A vector database is essentially a specialized type of search engine designed to handle complex, multi-dimensional data efficiently. At its core, it uses search indexes to enable an Approximate Nearest Neighbor (ANN) search process. When a user queries with a vector, the database searches for the most similar vectors, facilitating fast and efficient retrieval of related information.

Key Features of Vector Databases

These features make vector databases an invaluable tool for businesses that require dynamic, high-speed data interactions. For instance, e-commerce platforms can use vector databases to enhance product recommendations, thereby improving customer experience and engagement.

The most popular vector databases are Pinecone, Weviate and Chroma. And there are several great articles out there on how to use them properly.

Real-Life Examples of Vector Data

Vector data is pivotal in various advanced applications, demonstrating its versatility and power.

Table 3: Applications of Vector Data

These examples underscore how integral vector data has become in enhancing the functionality and user-friendliness of technology across different sectors.

Data is the future!

Vector databases stand at the forefront of a revolution in data management, particularly in how complex data is handled. By allowing for efficient storage, search, and retrieval of multi-dimensional data, they enable businesses and technologies to operate more dynamically and effectively.

As we continue to generate and rely on more complex types of data, the role of vector databases is set to grow, paving the way for more innovative applications and systems in the future. This technological evolution is not just enhancing current systems but is also setting the stage for future advancements in data management and analysis.

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The detection of fraudulent credit card transactions is an ideal candidate for the application of machine learning technology. However, in order to learn how to spot attempted fraud, such a system needs someone to tell it which historic transactions were OK, and which were not.

Excerpted from The AI Playbook: Mastering the Rare Art of Machine Learning Deployment,¹ by Eric Siegel (6 February 2024), published by The MIT Press.

Scott Zoldi fights crime across the globe. His superpower is data – and an unprecedented, innovative process to amass that data.

He’s got his work cut out for him. Every day, hordes of criminals work to exploit systemic vulnerabilities in how you and I shop. Their relentless work chips away at the very integrity of consumer commerce at large.

I’m talking about fraud. Crooks obtain your card details so that they can perform a transaction and make off with the spoils. In 2021, payment card fraud losses reached $28.58 billion worldwide.² The United States suffers more than any other country, accounting for more than a third of that loss. To make matters worse, fraud increased during the pandemic, in part due to the increase in “card not present” virtual transactions. Some called it the “scamdemic”.

Scott is FICO’s chief analytics officer. He oversees the world’s largest-scope anti-fraud operation. Day in and day out, his product Falcon screens all of the transactions made with most of the world’s credit and ATM cards – 2.6 billion cards globally. With Falcon, banks and other financial institutions can instantly block suspicious purchases and withdrawals.

This capability hinges on machine learning, and it demands an impressive dataset. A fraud-detection model must predict well, striking a tricky balance so that it recognises a lot of fraud and yet does so without incurring too many false positives.³ To this end, the data must fulfil exacting requirements. If you visualise the data as a simple table, just a big spreadsheet, it must be long, wide, and labelled. Here’s what I mean:

1. Long. You need data about real transactions – a lot of them. This list of many, many example cases from which to learn must be a long one. And by including a broad assortment of cases from around the world, the data can be representative. Each case composes a row of the data.
2. Wide. You need revealing information about each case, including behavioural characteristics of both the cardholder and the merchant. These are the factors on which a model will base its predictions. Since each row enumerates all these factors, the data is also wide. Each factor composes a column of the data.
3. Labelled. ML software needs many known examples of fraud from which to learn, prior transactions that have been designated as such. How do these cases get labelled? The fraudsters who perpetrated these crimes know which are which, but they have not, so far, been cooperative. This means we need humans on our side to manually label many examples. These labels typically make up the rightmost column of the data.

Such a data set sounds almost impossible to acquire. It could only be sourced from multiple banks across the globe. And even if you somehow convinced these institutions to cooperate and obtained a representative slew of example transactions, the fraudulent ones aren’t going to label themselves.

To obtain this data, Scott’s got to align the stars.

FICO Cultivates Data Without Borders

Scott has a PhD in theoretical physics from Duke University. And he’s formed a team of 70 more people with PhDs. Together, they generate the world’s de facto system for detecting fraudulent card transactions. You, I, and most people with payment cards are relying on them.

Scott’s anti-fraud operation isn’t what FICO is most widely known for. Along with another one of his teams, Scott also oversees this country’s most famous deployed model: the FICO Credit Score. Your FICO Score determines your power to borrow. It’s the most widely used credit score in the United States, employed by the vast majority of banks and credit grantors. It’s a household name, and many understandably feel that their FICO Score is a central part of their identity as a consumer.

But FICO’s fraud detection, which is normally invisible to us as consumers, affects us much more often. Named Falcon, this product is the biggest part of FICO’s software business and affects most of us almost every day, every time you use your card. FICO evaluates financial power by day, and fights financial crime by night.

To meet this responsibility, it’s important that the Falcon team gets the data it needs – some long, wide, and labelled data. To do so, it collects data from across a global network of banks.

This reliance on inter-enterprise data, collected from multiple companies, is atypical. Ordinarily, an ML project serves only the enterprise running the project. For such a project, internal data suffices, since the company has been tracking the very operations that the project aims to improve. In contrast, FICO isn’t a bank. It doesn’t process card transactions. Rather, it holds a rare, globally central, entrusted role across banks.

In 1992, Falcon was born of a radical move by a small group of banks. They decided to cooperate, rather than only compete. At the time, a tremendous portion of all credit card transactions – almost 1 per cent – were fraudulent. The fraud rate was only growing and threatened the entire industry. This looming crisis convinced financial institutions to overcome their raw capitalistic instincts and follow a call to arms for the universal good: to collaborate to fight crime, improve transaction integrity, and cut losses. Led by a company called HNC Software, they joined their data together, thereby multiplying their power to train fraud-detection models. Ten years later, FICO acquired HNC Software – and both Falcon and Scott Zoldi along with it.

Since then, Falcon’s consortium has grown to more than 9,000 banks globally, all continually sending in anonymised card transaction details. FICO receives about 20 billion records, amounting to terabytes of raw data, each month, a petabyte every five years.

Banks provide data to develop Falcon’s fraud-detection model and Falcon deploys that model for each bank.

Banks can’t benefit from Falcon without contributing to it. To be a FICO customer that uses Falcon, you must also join the consortium and share your data. Falcon has become so standard that, despite its cooperative nature, it’s a competitive necessity. To hold their position in the payment card market, banks need Falcon’s best-in-class fraud detection, which they can access only by cooperating. In the end, this levels the playing field. Even the smallest bank can deploy the very best fraud-detection model.

It’s Not Over Yet: Labelling the Data

In addition to tons of examples, Falcon’s training data needs another ingredient: labels that correspond with the model’s intended output. Each example transaction that makes for a row of data is incomplete until designated as either fraudulent or not fraudulent. Those labels will guide model training to do its job: generate a model that can discern positive cases from negative cases.

Only humans can provide the labels. For detection, we don’t get to benefit from “time will tell”, as we do when predicting a future event. Time has told whether a user responded when shown a certain ad or whether a debtor has defaulted. In those cases, we get the label “for free”. But for detecting a qualitative attribute for each case, such as whether it is fraudulent, each training example’s label can only be determined by a person.

Manual labelling is labour-intensive and expensive. The expense especially racks up when it requires subject matter experts, such as doctors for establishing whether each example indicates a certain medical diagnosis.

On the other hand, problems that don’t require special expertise, such as labelling traffic lights within images for an autonomous-driving project, can be outsourced on “crowd labour” platforms like Amazon Mechanical Turk for as little as a penny per case. But there’s a dark side: their largely unregulated working conditions “offer a bleak glimpse of what could come for a growing digital underclass”, according to Vocativ. Marketplace ⁴ calls this “the new factory floor of the digital age”.

To make matters worse, fraud detection requires an immense number of labelled transactions, because positive ones are rare. If the fraud rate is 0.1 per cent and you want the data to include at least 10,000 positive cases, then you need to label 10 million cases as to whether each is positive or negative.

Don’t fret! Falcon’s training data manages to sidestep this costly bottleneck by relying on what consumers do naturally. With card fraud, if the consumer sees an erroneous charge, they complain. We cardholders and our banks are, in effect, already doing all the grunt work to label many cases of fraud in the course of just living our lives.

A key reason that this approach works is that, with card fraud, banks can afford to learn the hard way. Since the detection system is imperfect, it allows some fraudulent transactions to go through. This generates a positive training case if the cardholder later complains about the unauthorised charge, even though it’s then typically too late to prevent the fraudster’s crime. The cost is absorbed by the bank, but the overall cycle is economically satisfactory. No humans were substantially harmed in the process of this data creation.

In other domains, you can’t do it that way. The missed, uncaught cases – false negatives – aren’t nearly as allowable for an autonomous vehicle that would drive through a red light or a medical system that would miss a diagnosis. In those domains, you often can’t avoid the need for additional manual work labelling many examples.

This “organic” labelling process for fraud detection, wherein people are essentially “following the money”, prioritises bigger cases of fraud over smaller cases. FICO treats only adjudicated fraud as positive cases, where the cardholder has formally certified that the transaction was fraudulent (whether it was them or the bank who noticed it in the first place). This means that suspected cases that never get adjudicated aren’t labelled as positive in the training data, even if the bank had to write off the charge. Since folks tend to bother with adjudication more for larger-value cases of fraud, lower-cost fraud is less often correctly labelled and is therefore effectively deprioritised by Falcon’s model. And that’s tolerable, since the false negative cost is lower for them.

On top of this manual labelling, many other positive cases are passively labelled – those that Falcon has spotted automatically. A bank using Falcon blocks an attempted fraudulent transaction and the cardholder might never even hear about it. This is almost a circular process, since that positive example will then serve to train an updated model for Falcon, which identified the positive case in the first place. However, once again, natural cardholder reactions help correct the data. If Falcon was wrong – if it is a false positive – then the cardholder, whose legitimate attempt to transact was blocked, will often take action to get it approved and the case will wind up as negative in the training data. In that way, what the model got wrong will serve to improve the next version of the model.

Altogether, this provides plenty of positive examples for Scott’s team. The number of labelled cases of fraud that they end up with approaches one million.

FICO Falcon Fights Fraud Fantastically

I consider Falcon one of the world’s most successful and widely impactful commercial deployments of ML. It screens all the transactions for 2.6 billion payment cards worldwide. That’s two-thirds of the world’s cards, including about 90 per cent of those in the United States and the United Kingdom. Seventeen of the top 20 international credit card issuers, all of the United States’ 100 largest credit-card issuers and 95 of the United States’ top 100 financial institutions, use Falcon.

Since its introduction, Falcon has reduced card fraud losses by more than 70 per cent in the United States. With the United States currently suffering around $10 billion in annual fraud losses, that reduction is saving that country alone something in the vicinity of $20 billion per year. 

For a detailed example stepping through the arithmetic to show how much money a bank might save by deploying a fraud detection model, see my MIT Sloan Management Review article “What Leaders Should Know About Measuring AI Project Value”.⁵ For more reading on payment card fraud detection in general, and FICO Falcon in particular, see this collection of citations.⁶

This article is excerpted from the book, The AI Playbook: Mastering the Rare Art of Machine Learning Deployment, with permission from the publisher, MIT Press. It is a product of the author’s work while he held a one-year position as the Bodily Bicentennial Professor in Analytics at the UVA Darden School of Business. For a complete bibliography for this article, see this PDF.⁷

About the Author

Eric Siegel, PhD, is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week⁸ conference series and its new sister, Generative AI Applications Summit,⁹ the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery”,¹⁰ executive editor of The Machine Learning Times,¹¹ and a frequent keynote speaker.¹² He wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die,¹³ which has been used in courses at hundreds of universities, as well as The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Eric’s interdisciplinary work bridges the stubborn technology / business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice.¹⁴

References

1. http://www.bizml.com/
2. https://nilsonreport.com/newsletters/1209/
3. https://sloanreview.mit.edu/article/what-leaders-should-know-about-measuring-ai-project-value/
4. https://www.marketplace.org/2021/05/04/the-human-labor-behind-artificial-intelligence/
5. https://sloanreview.mit.edu/article/what-leaders-should-know-about-measuring-ai-project-value/
6. https://predictionimpact.com/documents/notes-for-The-AI-Playbook/The AI Playbook – notes for chapter 4.pdf
7. https://predictionimpact.com/documents/notes-for-The-AI-Playbook/The AI Playbook – notes for chapter 4.pdf
8. https://www.machinelearningweek.com/
9. https://generativeaiworld.events/
10. http://machinelearning.courses/
11. http://machinelearningtimes.com/
12. http://www.machinelearningspeaker.com/
13. https://www.machinelearningkeynote.com/predictive-analytics
14. http://www.civilrightsdata.com/

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Today, well-versed healthcare analytics software typically consists of a DWH as a central component in the industry of medicines. In this piece, you will explore how it empowers every aspect of dental innovation and research.

What Is Meant By Open Data Source?

Open data involves information that any organization can implement in any preferred manner. It offers the opportunity to transform, organize, share, and create both commercial and non-commercial applications of the technology. Open data, a broader drive in technology, has emerged alongside open-source hardware and software.

Why Open Data Warehouse Is Leading Innovation in Healthcare

There are several reasons why an open data warehouse has landed as a leading and lucrative choice when it comes to the all-encompassing advancement of modern healthcare. Some of the specific reasons are listed below:

• Combination of data from multiple sources
• Unconventional approach-integrated PMS
• Executive, management, and operational reporting needs for reporting and business intelligence
• Sustained cost-saving technology
• Datacenter for ultimate expandability
• Integration for machine learning
• Platform to combine data from a range of sources such as labs, vendors, HR, and PMS
• Tableau, Data Studio, Microsoft BI, and Looker connectors

Big Data – A Boon to Oral Health Research and Development

Big data in biomedicine is often collected from various sources, including social media, wearable wearables, medical records, and health research. The multitude of insights unleashed in biomedical big data is being transformed into evidence-based, implementable schemes to enhance population health and overall well-being. It has been made much more effortless by the latest developments in data amalgamation, storage, and analyzing techniques.

The gradual adoption of electronic medical record systems, unstructured clinical data, scattered interactions between data silos, and the certainty that dental health is distinct from overall health are the primary reasons for the delay in the advantages of biomedical big data in modern dentistry.

The Contribution of Data Warehousing In the Groundwork of Healthcare

The healthcare sector is increasingly emphasizing data analytics each year than ever before. The healthcare considerable data market value was estimated at $11.5 billion in 2016 and is expected to sum up to about $70 billion by 2025, as per research, in less than nine years, which can be considered a six-fold growth over the previous.

Medical firms find it more effortless to manage the booming amounts of data. Thanks to the latest technology, such as cloud computing and machine learning. Nevertheless, one must first organize and compile the information from various resources to obtain high-quality insights. That intermediate objective can be achieved using a robust healthcare data warehouse.

• Enhanced clinical decisions
• Efficient reporting
• Strategic treatment plans
• Optimized insurance payments and claims
• Enhanced patient experience and outcomes
• Personalized value-based care

In Conclusion

Data warehousing has worked with a number of established healthcare organizations and startups, building health-tech solutions of various kinds. A large percentage of organizations and firms in healthcare are realizing the importance of data warehousing in dentistry and data science when it comes to implementing the most high-end technologies in contemporary and intricate healthcare operations. The technology is being embraced by small, medium, and large-scale firms in the healthcare sector today. Incorporating data warehousing strategies in your healthcare practices can directly and significantly impact business productivity. 

The post Unlocking Insights: How Open Data Warehouses Empower Dental Research and Innovation appeared first on The European Business Review.

How minimalism applies to minimalist data visualization

When it comes to minimalist data visualizations, the principle of “less is more” often rings true. Rather than inundating viewers with flashy graphics, 3D effects, and overly complex multidimensional data, the most insightful data visualizations frequently embrace simplicity and restraint. Minimalist data visualization highlights the most essential trends, relationships, and conclusions from the dataset. This requires thoughtful curation – choosing which dimensions and variables to plot while leaving out nonessential parameters. An effective minimalist data visualization should reduce noise, clutter, and distraction to bring the core message into sharp relief.

The judicious use of color, clean and open space, intuitive layouts, and sparse labeling allows viewers to parse the visualization and grasp the key takeaways easily. Just as a minimalist architect strips away ornamentation to reveal the fundamental beauty of a structure, minimalist data visualization aims to reveal the underlying story in the data through graceful visual simplicity.

Principles of minimalist data visualization

• Clarity and Comprehension: One of the cornerstones of minimalist data visualizations is its unwavering focus on clarity. This principle ensures that even the most complex datasets are distilled into their simplest form, making the data not only accessible but also meaningful to the viewer.
• Focus: The spotlight shines on the most crucial data points in minimalist data visualizations. This selective focus makes minimalist data visualization so powerful, as it guides the viewer’s attention to the heart of the matter, ensuring the core message is never lost in translation.
• Efficiency: Efficiency in minimalist data visualizations is about respecting the viewer’s cognitive load. By presenting data cleanly and uncluttered, simple data visualization minimizes distractions, allowing for a smoother and more intuitive data interpretation experience.
• Integrity: Minimalist design data visualization is grounded in honesty. This straightforward approach to data presentation builds trust with the audience, leaving little room for misunderstanding or manipulation, ensuring the data’s integrity remains intact.

Techniques for creating minimalist data visualizations

1. Negative space in minimalist data visualization

Negative space is a critical component of minimalist data visualization. It’s about using emptiness effectively to highlight the data that matters, an approach that can significantly enhance the viewer’s focus and understanding.

2. The right graphs for minimalist data visualization

Choosing the appropriate type of graph is pivotal in simple data visualization. This decision can make or break the clarity of the data presentation, as the simplest graph often tells the story best.

3. Color in minimalist data visualizations

In minimalist design data visualization, color is used judiciously to guide and inform. This restrained use of color is crucial in maintaining simplicity and enhancing the viewer’s ability to comprehend the data at a glance.

4. Simplification of labels in minimalist data visualizations

Simplifying labels and legends is essential in minimalist data visualization. This technique ensures that the text aids understanding without overwhelming the viewer, keeping the visualization clean and focused.

5. Decoration in minimalist data visualization

Minimalist data visualization advocates for the removal of unnecessary decorations and gridlines. This simplicity ensures that the data remains the focal point, free from distraction.

Applying minimalist data visualization in various fields

Minimalist data visualization finds its application across numerous domains, from business intelligence and corporate reporting to academic research, public policy, and digital product design. Rather than cluttering charts with excessive details, colors, dimensions, and other visual elements, embracing simplicity and minimalism allows viewers to grasp the core message easily. Minimalist data visualization aims to highlight the most essential insights, patterns, and trends by eliminating nonessential design components.

This clarity and focus makes even highly complex data sets relatable and memorable. For example, a minimalist bar chart tracing a key business metric over time allows readers to spot key inflection points instantly. Similarly, a minimalist plot visualizing the correlation between two variables draws attention to the relationship rather than extra points. Great minimalist visuals are like powerful poems – they capture profound truths through simplicity. This ethos of conveying more by showing less makes minimalism a cornerstone of impactful, ethical, and elegant data visualization.

Conclusion

Embracing minimalist data visualization is more than a stylistic choice; it’s a commitment to clarity, understanding, and integrity in data presentation. As we have explored, the principles and techniques of minimalist data visualization offer a pathway to more impactful and insightful data storytelling. In the pursuit of simplicity, minimalist data visualization emerges not just as a method but as a philosophy, reminding us that in the world of data, simplicity can reveal the profound truths hidden within the numbers.

The post Keeping It Simple: The Power of Minimalist Data Visualizations appeared first on The European Business Review.

Identifying Key Data Sources for Efficient Ingestion

Streamlining the data ingestion process begins with a strategic approach to identifying the most valuable data sources. This requires a thorough understanding of the organization’s objectives and the data types that will drive actionable insights. Consider the following points when pinpointing key data sources:

• Relevance: Focus on sources that provide data directly aligned with your business goals.
• Quality: Prioritize high-quality data sources to ensure accuracy and reliability in your analyses.
• Accessibility: Assess the ease of access to the data, as some sources may have restrictions or require specific protocols for ingestion.
• Volume and Velocity: Understand the scale and flow rate of the data to prepare for appropriate ingestion methods and tools.
• Format and Structure: Identify the format (structured, semi-structured, unstructured) and structure (schema) of the data to determine the necessary preprocessing steps.

By carefully selecting data sources that meet these criteria, organizations can optimize their ingestion process, reduce overhead, and ensure that the data being ingested will provide the most value to the business.

Automating Data Collection: Tools and Techniques

According to experts at CandF.com, embracing automation in data collection is not just about efficiency. It’s about scalability and accuracy. Tools like ETL (Extract, Transform, Load) platforms, web scrapers, and APIs have revolutionized how businesses approach data ingestion. By leveraging these tools, organizations can minimize manual errors and ensure that data is collected consistently and repeatedly. Selecting the correct set of tools that integrate seamlessly with your existing systems and can handle the volume and variety of data your business encounters is crucial.

Advanced techniques such as machine learning algorithms can further refine the data collection process. These algorithms can be trained to identify patterns and anomalies in data, which can then be used to automate the collection of high-quality data sets. Moreover, implementing a robust data governance strategy ensures that the automated data collection meets compliance standards and privacy regulations. This is particularly important in industries where data sensitivity is paramount, such as healthcare and finance.

In conclusion, the successful automation of data collection hinges on the strategic selection and implementation of tools and techniques that align with your business goals. Organizations can achieve a competitive edge by prioritizing data quality and compliance and continuously refining the automation process. The ultimate goal is to create a data ingestion process that is efficient and adaptable to the evolving landscape of data sources and regulatory requirements.

Ensuring Data Quality at the Point of Entry

Maintaining high data quality from the outset is crucial for any data ingestion framework. Implementing rigorous validation rules and real-time checks is essential to catch inaccuracies, inconsistencies, or incomplete data before they enter your system. Employing techniques such as schema validation, data type checks, and constraint enforcement can significantly reduce the need for later data cleansing efforts. Moreover, setting up automated alerts for anomalies or undefined values ensures that issues can be addressed promptly, maintaining the integrity of the data pipeline and facilitating smoother downstream processing.

Optimizing Data Ingestion Pipelines for High-Volume Processing

When dealing with high-volume data processing, efficiency and scalability are paramount. To optimize your data ingestion pipelines, evaluating the architecture for both performance and reliability is essential. Utilizing distributed systems can help handle large data streams by partitioning the data across multiple nodes, thereby increasing throughput and fault tolerance. Additionally, implementing backpressure mechanisms ensures that the system can gracefully handle surges in incoming data without overwhelming the processing capabilities.

Streamlining the transformation and enrichment stages of your pipeline can significantly enhance performance. Consider the following points:

• Minimize data processing steps by consolidating transformations and avoiding unnecessary data movement.
• Employ in-memory processing technologies like Apache Spark to reduce latency and speed up data handling.
• Opt for schema-on-read approaches for more flexibility and agility in managing diverse data types.

These strategies can lead to a more efficient pipeline capable of handling high volumes of data with reduced processing times.

Monitoring and fine-tuning are critical components of an optimized ingestion process. Implement real-time monitoring tools to track the performance of your data pipelines and identify bottlenecks promptly. Regularly review and adjust configurations such as batch sizes and buffer capacities to align with the current data load. By establishing a feedback loop that informs continuous improvement, you can ensure that your data ingestion pipelines remain robust and agile, even as data volumes and velocities evolve.

Leveraging Cloud-Based Solutions for Scalable Data Ingestion

Cloud-based solutions have become a cornerstone for businesses seeking to enhance their data ingestion capabilities. By utilizing the cloud, organizations can benefit from elastic scalability, which allows them to handle varying volumes of data without needing upfront investments in physical infrastructure. This flexibility is critical for companies that experience fluctuating workloads or rapid growth. Moreover, cloud providers typically offer a suite of integrated tools that can streamline the ingestion process, from data extraction and transformation to loading. However, reliance on cloud services also introduces potential concerns, such as data security and compliance with regulations, which must be carefully managed.

One of the key advantages of cloud-based data ingestion is the ability to implement automated scaling and load balancing. This ensures that resources are efficiently utilized and performance remains consistent, even as data demands increase. Cloud services often provide robust disaster recovery and data backup solutions essential for maintaining data integrity. Conversely, companies must consider the costs associated with cloud services, as pricing models can be complex and may lead to unexpected expenses if not monitored closely. It’s also essential to evaluate the network latency and bandwidth requirements, as these can impact the speed and reliability of data ingestion.

Another significant aspect of cloud-based data ingestion is the ecosystem of services that can be leveraged to enhance data processing. For instance, many cloud providers offer services for real-time data streaming, advanced analytics, and machine learning, which can be seamlessly integrated into the ingestion process. This integration can unlock powerful insights and drive innovation within an organization. Nevertheless, ensuring that the chosen cloud platform aligns with the company’s technical expertise and that staff are adequately trained to manage and optimize the data ingestion pipeline is crucial. At the same time, cloud-based solutions offer numerous benefits but require a strategic approach to realize their potential fully.

Monitoring and Maintaining Data Ingestion Systems for Long-Term Success

Ensuring the reliability and efficiency of data ingestion systems is crucial for businesses that depend on timely and accurate data analysis. Regularly monitoring these systems is not just about troubleshooting; it’s about proactively managing the data flow to prevent issues before they arise. This involves comprehensive logging and alerting mechanisms that can provide insights into performance bottlenecks, data quality issues, and system failures. By keeping a close eye on these metrics, organizations can quickly identify and address problems, minimizing downtime and ensuring consistent data availability.

Organizations should follow a set of best practices to maintain a robust data ingestion framework.

1. Implement automated testing to validate data integrity and ingestion workflows continuously.
2. Version control for ingestion pipelines to track changes and facilitate rollback in case of errors.
3. Regularly update and patch ingestion tools and platforms to protect against security vulnerabilities and improve performance.

These steps help create a resilient system that can adapt to changing data sources, formats, and volumes without compromising data quality or processing speed.

Long-term success in data ingestion is about more than just the technology but also the people and processes involved. Establishing a culture of continuous improvement where team members are encouraged to suggest and implement enhancements to the ingestion process can lead to significant gains in efficiency. Additionally, it’s essential to have a scalable architecture that can grow with the business needs, avoiding the pitfalls of a system that becomes obsolete or insufficient over time. By investing in training and development for the team and choosing scalable solutions, organizations can ensure their data ingestion systems remain robust and agile for years to come.

The post Streamlining Your Data Ingestion Process: Best Practices and Tips appeared first on The European Business Review.

Over 90% of organisations have data initiatives planned for 2023, yet Tamr research has shown that more than half are still facing challenges in realising business value from their company data. The reason for this is that these organisations fail to take one necessary action – treating their data like a product.

Financial services are sitting in one of the most data-rich industries. However, despite all this data on offer, traditional organisations in this field are not fully utilising this data to reap maximum value, provide personalised services to clients and address business challenges such as fraud. Here in fact is a lesson to be learnt from fintech challengers. Product strategies have significantly helped these organisations to thrive and apply similar processes to make the most of the data. 

The importance of data products

So, what are data products? Simply put, they are the best version of data. They are accessible, trustworthy and high-quality, meaning they can be applied to business challenges to produce effective outcomes. For financial services in particular, data products generate a structure to the processes, technology and ownership necessary to provide businesses with clean, curated, continuously updated data.

For an organisation in this industry to build, implement and continually develop a successful data product strategy, it should follow these 5 key considerations.

1. Think about why

Before starting to create a data product strategy, a financial services organisation needs to define the objectives that it wants the business to achieve. This means starting small and identifying a specific aim which reflects the organisation’s priorities, such as simplifying to lower costs or transforming business portfolios for growth. From here, a company can align with stakeholders from IT, leadership and line of business (LOB). They will all be working together and so cooperation is crucial to success. 

For any financial services companies that are not certain on where to start, it is recommended to use customer data. From here, so many opportunities can be created by utilising customer data products, from increased visibility of customer journeys, to improved customer experience, targeting and conversion rates.

2. Understand your data

Now the objective has been defined, an organisation will need to determine whether it has the capacity to implement a data product strategy. To do this it will need to understand where the data lives, if it is accurate and complete, how often it is updated and whether it is integrated across departments. This shows the true quality of the data at hand and allows to budget resources effectively.

From here, a business can begin to develop the team and assign responsibilities to build and manage the development of a data platform across departments and identify research gaps early.

3. Outline your use case

Now, it’s time to begin – and this starts by outlining a use case based around existing challenges. This ensures the data product actively influences business outcomes. It is likely that the leaders of different lines of business, for example, marketing, R&D, or procurement, have a problem that is preventing them from reaching their respective objectives. Defining a use case can allow the organisation’s leaders to focus on increasing revenue, controlling costs, or managing risks. If it can be clearly explained why a data product is needed to the leaders in an organisation, that will help them to piece everything together and understand how the data product will benefit them.

4. Obtain leadership buy-in

In order to progress with a use case, the appropriate funding, support and resources to back the project must be ensured. Sharing a use case and roadmap will make it clear how value will be demonstrated in the short term and how the data product will evolve to increase its value.

It should also outline how to best measure success, and this should be done with KPIs that align with goals and show everyone what success looks like for that financial services organisation.

5. Establish a minimum viable data product

Once budget and buy-in is secured, it’s time to implement the strategy. This means developing a minimum viable data product (MVDP). With this, start small and deliver a few more capabilities and a little more value with every data product released. This will drive the adoption of the data product with the eventual aim of securing additional resources and funding in the future.

It is important to rely on LOB partners and their understanding of how to use the data product and apply it to existing working practices. That’s where expertise is crucial, as it can show them how a data product strategy can enhance processes. Finally, absorb all feedback and apply it to future releases to generate even greater value for end users.

The true value of your data

Data products are truly the best version of data. By learning from these key 5 recommendations for success, business leaders in financial services will be able to create more meaningful solutions and better compete with fintech challengers, as well as other more traditional competitors in the playing field. 

In order to innovate, those in financial services must recognise data as an asset and see a data product strategy as a crucial component to generate greater value for all. By doing so, these businesses can evolve and offer personalised services to customers and address business challenges, to reduce risk and facilitate long-term growth.

This article was originally published on 14 May 2023.

About the Author

Suki Dhuphar is the current head of EMEA at Tamr. Suki has over 17 years of experience in the telecommunications and software industries. Suki has worked with some of the world’s largest companies, including Comverse, Martin Dawes Systems, Hansen Technologies, and Servista. In their current role, they are responsible for managing Tamr’s relationships with its European customers. Suki has a deep understanding of the needs of their customers and is able to provide them with creative solutions to their unique problems.

The post 5 Steps for Building a Successful Data Product Strategy appeared first on The European Business Review.

We understand that artificial intelligence (AI) resulted in the loss of many blue-collar jobs as smart robots took over the manufacturing process. However, we now know that this generation of AI will have even greater impact in truly creative fields, including art and original authorship. The impact is far-reaching and revolutionary.

Decades ago, it was broadly predicted that digital machines would sweep the workforce world, leaving precious few, mostly white-collar-worker, positions. The professionals, managers, and executives would continue to work in their higher-paid, benefits-laden positions; the rest of the workforce would be replaced by AI. At first, in large part, the advent of AI did impact the non-professional positions at a higher rate than the skilled professions. Economist and public policy analyst Professor Harry J. Holzer of Georgetown University writes, “Indeed, digital automation since the 1980s has added to labour market inequality, as many production and clerical workers saw their jobs disappear or their wages decline. New jobs have been created – including some that pay well for highly educated analytical workers. Others pay much lower wages, such as those in the personal services sector.”³

In the intervening years, the AI field has been refined and more development has been made in applications such as unsupervised machine learning, natural language processing (NLP), natural language generation (NLG), reinforcement learning, and neural network learning. These tend to emphasise assisting human professionals, so that they can perform at higher levels, accomplishing results that previously were not possible, rather than taking on the work of an entire class of co-workers.

Concerns in this area of job replacement come at a time when researchers are predicting as much as a 98 per cent chance of a global recession in 2023.¹ In this changing environment, the benefits of AI go beyond replacing static, repetitive jobs – such as assembly line workers – with robotics. An inevitable result is that there will be a cutback in the employee workforce in areas most affected by the recession, while at the same time greatly enhancing opportunities for innovation and out-of-the-box thinking. In researching the COVID-19 related downturn and what can be expected thereafter, the Brookings Institution reported:

As for what all of this means for the future, the potential of an automation surge reinforces the fact that any coming recession won’t only bring an end to the nation’s plentiful supply of jobs. Any downturn is likely to bring a new bout of structural change in the labour market and its demand for skills. If it extends for a while, the downturn could induce firms in food service, retail, and administrative work to restructure their operations toward greater use of technology and higher-skilled workers. For America’s beleaguered lower-skill workers, these changes will complicate the return to normalcy.⁶

AI developers are working feverishly to meet anticipated needs to stabilise or reduce costs for business and industry. Artificial intelligence has advanced in ways that were unanticipated by most observers in the twentieth century. As reported in the US Department of Labor Monthly Labor Review for July 2022:

New computing capacities – in areas such as image recognition, robotic manipulation, text processing, natural-language processing, and pattern recognition, and, more generally, the ability to learn and improve rapidly in relatively autonomous ways – represent a break from the hand-coded, rules-based programs of the past. In this view, newer robots and AI represent a clear departure from previous waves of computing, one that accelerates the pace of technological change and job displacement.²

I must admit that I had anticipated that the rapid growth of sophisticated analytical and predictive AI capabilities would be the primary workforce impact in the near term. Driven by the massive data collection of the twenty-first century, we are ripe for harvesting these data in useful ways to drive higher profits and greater efficiencies in operations and logistics. However, most surprising to me is the burgeoning growth of the creative applications of AI in the areas of original copywriting, graphics, and art. These creative, rather than purely analytical, applications seemed further down the road for AI, yet they already have a place in business and industry that is growing stronger every day.

Among the technologies that have grown are those driven by Generative Pre-trained Transformer 3 (GPT-3). Developed by Open AI, that rather ambiguous title covers a range of functional tools that, among other talents, conduct research gathering information from across the internet without supervision and “write” amazingly cogent copy that can be used in news releases, product promotions, operation manuals, and even newspaper and periodical articles. In writing an article¹⁰ on the topic of AI in higher education, I sampled a number of platforms using GPT-3. After describing my goals for the article and filling in a very brief outline, I submitted it to https://copy.ai. A 10-paragraph, nearly 700-word article appeared in just a few seconds. I saved a few sentences of the text written by Copy.ai as an example of the writing:

The world we live in is already being reshaped by artificial intelligence. The technology is changing the way we work, learn and interact with each other, but it’s also perpetuating inequality. As institutions of higher education that are tasked with preparing students for the future of work, colleges and universities have a unique opportunity to help shape this future to be more equitable.

I had not mentioned in my brief submission to the Copy.ai program anything about inequality or the responsibility of educators to ensure that learners are prepared for the less obvious AI ramifications for equity within the workplace. AI picked up on that omission in a second, literally. The remainder of the 10-paragraph essay was equally lucid and cogent.¹⁰ News publications, including Bloomberg and Associated Press among many others, regularly use AI to produce sports and news reports.⁸ Almira Osmanovic Thunström, writing in the Scientific American, reported that she has submitted a GPT-3-written academic article to peer-review.¹¹ Clearly, momentum in original AI research and writing of articles is building, and AI’s role in original writing is already proven.

Specific to business uses, natural language processing (NLP) is a function of AI that enables it to create copy. It powers a host of applications. There are now dozens of AI copywriting tools that promise to do everything from optimising your content for SEO to writing creative product descriptions, to creating video scripts and social media posts.⁵

In business applications, given a modest set of instructions by humans, the algorithm goes about gathering results from selected databases or the internet at large and, within seconds, it assembles multiple sample products that can be refined and disseminated. This ability to “create” truly original products, from text to multimedia to art, is at the forefront of AI today. No longer about robots replacing assembly-line workers, the emerging frontier is much more about creativity and visioning.

Perhaps most striking of the emerging, commonly available, AI capabilities is seen in DALL-E 2, developed by Open AI (which also developed GPT-3). Using brief text descriptions, the algorithm instantly creates stunning original, creative artwork.⁷ As a society, we have yet to fully catch up with the current state of AI. The New York Times reports on some of the challenges presented by the advent of AI in competitions, such as the case in which an AI-created artwork was submitted in a competition. It took first place in an art competition in Colorado; not all of the others in the competition were happy about competing with AI. ⁹

While the images created by AI are stunning, perhaps even more useful day to day is the advent of AI unique coding applications from simple descriptions. Text-to-code generation is now offered by a host of vendors featuring coding on demand by describing the outcome of what you require to be coded. No knowledge of the computer language or programming approach is required; one simply describes what you want the new app to do, and AI writes the code.⁴ The iterative quality of AI-coding AI applications that can in turn code even more unique AI apps is a bit mind-boggling to me.

Where do these innovations lead? Certainly, today they are providing dramatic new ways to cut costs, shorten timelines, and enhance productivity while creating new product lines. Over time, as the algorithms improve and expand, we will see whole new approaches to the creative research and development areas of the economy. The creation of original text and graphics has already impacted marketing, media, and related fields. The coding assistance and creation models will enable us to bridge the disconnect we often find between the creative vision and the coding reality. The outcome, too often, does not today precisely match the vision. The result of this direct connection between person and machine may be a much tighter relationship between the creative visioning and the production side of operations.

With open models leading the way, we are likely to see these new technologies adopted and utilised even under the threatening recession. They offer a bright light illuminating an unprecedented future surge of innovation and responsive production.

This article was originally published on 31 January 2023. 

About the Author

Ray Schroeder is Senior Fellow at the University Professional and Continuing Education Association (UPCEA) and Professor Emeritus at the University of Illinois Springfield (UIS). A frequent speaker and author, Ray writes the biweekly “Online: Trending Now” column in Inside Higher Education and distributes multiple daily curated reading lists for UPCEA.

References

1. Egan, M. (28 September 2022). “There’s a 98% chance of a global recession, research firm warns”. CNN. https://www.cnn.com/2022/09/28/economy/recession-global-economy/index.html
2. 2Handel, M.J. (13 July 2022). “Growth trends for selected occupations considered at risk from automation”. Bls.gov. https://www.bls.gov/opub/mlr/2022/article/growth-trends-for-selected-occupations-considered-at-risk-from-automation.htm
3. Holzer, H.J. (9 March 2022). “Understanding the impact of automation on workers, jobs, and wages”. Brookings. Retrieved 30 October 2022, from https://www.brookings.edu/blog/up-front/2022/01/19/understanding-the-impact-of-automation-on-workers-jobs-and-wages/
4. Janakiram, M.S.V. (14 March 2022). “5 AI tools that can generate code to help programmers”. Forbes. https://www.forbes.com/sites/janakirammsv/2022/03/14/5-ai-tools-that-can-generate-code-to-help-programmers/?sh=24d0449f5ee0
5. Liza. (26 February 2022). “5 best AI copywriting tools in 2022 [out of the 11 we tested]”. P2P Marketing. https://peertopeermarketing.co/ai-copywriting-tools/
6. Muro, M., Maxim, R., & Whiton, J. (24 March 2020). “The robots are ready as the COVID-19 recession spreads”. Brookings. https://www.brookings.edu/blog/the-avenue/2020/03/24/the-robots-are-ready-as-the-covid-19-recession-spreads/
7. OpenAI. (14 April 2022). Dall·e 2. OpenAI. https://openai.com/dall-e-2/
8. Peiser, J. (5 February 2019). “The rise of the robot reporter”. New York Times. https://www.nytimes.com/2019/02/05/business/media/artificial-intelligence-journalism-robots.html
9. Roose, K. (2 September 2022). “An A.I.-generated picture won an art prize. Artists aren’t happy”. New York Times. https://www.nytimes.com/2022/09/02/technology/ai-artificial-intelligence-artists.html
10. Schroeder, R. (24 August 2022). “Higher ed, meet GPT-3: We will never be the same!”. Insidehighered.com. Retrieved 3 November 2022, from https://www.insidehighered.com/digital-learning/blogs/online-trending-now/higher-ed-meet-gpt-3-we-will-never-be-same
11. Thunström, A.O. (30 June 2022). “We asked GPT-3 to write an academic paper about itself—then we tried to get it published”. Scientific American. https://www.scientificamerican.com/article/we-asked-gpt-3-to-write-an-academic-paper-about-itself-mdash-then-we-tried-to-get-it-published/

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For businesses competing in this bustling market, developing a rapid go-to-market strategy is crucial for staying ahead. A vital part of this strategy is effective product data syndication, ensuring quick and effective customer communication. The importance of this approach to speeding up market entry cannot be overstated.

At Gepard PIM, we understand the significance of a quick go-to-market strategy and the essential role of efficient product data syndication. This article will provide practical tips on data syndication to help streamline your market entry. Keep reading for actionable insights.

What is Product Data Syndication?

Product data syndication is the strategic distribution of detailed product information to multiple eCommerce channels, platforms, and marketplaces, each with unique requirements. This process includes sharing specifics like product descriptions, pricing, images, and specifications in a compatible and optimized format for each individual sales channel. Doing so ensures that the data is accurately represented and effectively utilized across various online retail environments.

The Significance of Product Data Syndication

The significance of product data syndication (PDS) in the current digital business landscape is multi-faceted.

• Supply Chain Efficiency. Effective PDS ensures the timely and accurate distribution of product details across platforms, avoiding delays and inconsistencies. This leads to more efficient order processing and improved customer satisfaction​​.
• Regulatory Compliance. PDS aids in meeting regulatory standards in highly regulated industries by ensuring clear and accessible product information, thus avoiding compliance issues​​.
• Enhanced Partner Relationships. Accurate and comprehensive product data fosters trust and reduces errors in content between businesses and partners, improving sales figures​​.
• Faster Product Introduction. A robust PDS strategy enables the quicker and more efficient introduction of new products to the market, significantly benefiting the competitive positioning of businesses​​.
• Cross-selling and Upselling. PDS can recommend related products based on customer history, boosting order value and encouraging additional purchases. It also creates opportunities for upselling by highlighting products with superior features or benefits​​.
• Reduced Operational Costs. By streamlining the data exchange process and reducing inaccuracies, PDS helps cut costs and allows for the reallocation of resources to more strategic operational areas​​.
• Improved Customer Experience. Detailed product descriptions and high-quality images enhance the shopping experience, increasing engagement, conversion rates, and loyalty. In B2B contexts, providing partners with accurate product information optimizes the value chain, reducing errors and delays​​.

Top 6 Product Data Syndication Tips

1. Consolidate Your Data Sources

Begin by merging all your data sources into a single source of truth. This includes retiring outdated databases and combining spreadsheets and shared documents into a unified format. Utilize next-gen Product Information Management (PIM) software to adapt data to your specific attribute hierarchy. This centralization is foundational for effective product data syndication, streamlining management, and updating​​.

2. Master Data Mapping

The data mapping module guides the organization of product attributes. This step is where the complexity of multiple spreadsheets and databases is transformed into a cohesive database. Data mapping software can split a single brand’s product attributes into a set tailored for specific sales channels or merge different product attributes into one comprehensive attribute, ensuring uniformity across platforms​​.

3. Prioritize Data Quality Assessment

Once the data is integrated, use the PIM to evaluate the quality of your product data. This built-in feature filters whole catalogs by the products that need editing first, ensuring that data quality is consistently high.

4. Automate Data Clean-up Processes

Simplify corrections and enhancements to product data through automation. Utilize bulk update functions in your PIM software for efficient data management, reducing manual effort and increasing accuracy​​.

5. Enable Cross-Departmental Data Access

As you refine product data, ensure other departments such as product development, web development, marketing, and customer service have the necessary access. Next-gen PIM software offers customizable user permissions, allowing you to control who sees what and who can edit, ensuring data is used optimally across your organization.

6. Accelerate Data Distribution

Use the PIM’s channel management features to select the exact version of the data you need when distributing updated data to sellers or platforms. This ensures that the latest, most accurate product information is always used, keeping your offerings current and competitive.

Product Unit Mapping & Transformation Best Practices

Product unit mapping involves correlating units of measure used within your business to those used by different sales channels or marketplaces. This process ensures that product measurements, sizes, and quantities are accurately and consistently represented across all platforms.

Implementing Transformation Strategies

To effectively transform and standardize product units, it is necessary:

• Identify Variances: Recognize the different units of measure used across various platforms.
• Create Conversion Rules: Develop rules for converting units into a standard format.
• Automate the Process: Utilize software solutions that can automatically convert and update unit measurements as per channel-specific requirements.
• Ensure Accuracy: Regularly review and update conversion rules to maintain accuracy in product listings.
• Streamline Communication: Clearly communicate these standards within the organization and with external partners to ensure consistent application.

By meticulously mapping and transforming product units, businesses can avoid confusion, enhance customer understanding of products, and maintain consistency in product information across diverse sales channels.

Conclusion

Effectively using product data syndication is key to success in today’s digital market. By applying these strategies, businesses are not just keeping up but are also set to lead in the online retail world. In a marketplace driven by data, those who skillfully handle and share their product information will lead the way, opening up new opportunities and fostering growth.

The post Top 6 Data Syndication Tips to Accelerate Your Go-to-Market Strategy appeared first on The European Business Review.

As Europe sets out to deliver on its second digital agenda, companies will need to equip themselves with a modern network that can enable next-generation technology solutions. However, our research shows that mere investment is not enough. Enterprises need to value modern network as the foundation of digital transformation, sync network strategy to C-Suite agenda, close the talent gap through creation vs. only recruiting, and most importantly refocus budget in favour of new modern networks instead of repairing legacy ones.

Europe is continually at the forefront of advancing the region’s digital infrastructure and accelerating the digital transformation of businesses. The first 10-year digital agenda for Europe (2010-2020) identified for the first time the key enabling role of information and communication technologies (ICTs) in reaching the region’s goals and setting out specific provisions to ensure a fair, open, and secure digital environment. Now, Europe is rolling out the second digital agenda (2020-30), setting itself challenging targets in various digital domains for 2030. It calls for advancements in areas such as quantum computing, blockchain, human-centric and trustworthy AI, semiconductors, digital sovereignty, cybersecurity, gigabit connectivity, 5G, and 6G. These are critical for the region’s digital transformation and to maintain its global competitiveness.

Modern connectivity is the foundation on which the realisation of this vision will rest. Companies across the globe acknowledge that a modern network is a prerequisite for transforming the business, enabling better outcomes for companies’ workforce, customers and partners. Our latest Accenture research “Modern networks: How to fast track competitive advantage in the digital future”¹ shows that leading global companies are making sizeable investments in networks. European companies are spending close to 24% of their total IT budget to bolster network connectivity. Looking into the future, 73% plan to grow network investments by 6% or more during 2022-25, as compared to 46% during 2021-22. This aligns with the vision of Europe’s second digital agenda.

However, our research shows that these investments do not translate into improved network performance; more than half of European enterprise executives continue to face issues with their legacy networks, with a significant impact on the company. These include security breaches, high cost of deployment, inconsistent availability and low capacity. The impact of these network issues cascades across all business domains of an enterprise, leading to significant risk exposure.

Our analysis in Figure 1 shows that even companies investing a higher percentage of their ICT budget in network are still exposed to significant business risks attributable to network deficiencies.

About two-thirds of European enterprises in our study are exposed to risks in six areas: technology effectiveness, business efficiency, customer experience, workforce, trust and privacy, and sustainability. This means that network issues are not only obstacles to achieving business goals; they pose a significant risk in multiple domains.

Interviews with experts in European enterprises also confirm these challenges, which will only increase as businesses’ network requirements grow in the future. One of the executives said, “We’re in an urgent moment right now. We have a lot of aging network and wireless access points, and the need for bandwidth is going to be increasing for us within the next year. So, are we going to be able to meet that demand and is that going to have a negative impact on the service?”

So the question now is: If network spending is increasing, why is the network-associated company risk factor still so high?

The answer is that a majority of this investment goes towards the maintenance of legacy networks. Evaluation of the maintenance and modernisation budget for the last four years shows that while the share of investment in network maintenance activities is on the decline, the pace of change is too slow, and maintenance still accounts for almost half of the overall network budget of companies.

So, simply investing more is not the answer. Companies must refocus the budget in favour of new modern networks instead of repairing old, legacy ones. A good example of how a company benefited from flipping the budget in favour of new, modern networks instead of repairing old, legacy ones is that of British Sugar.

The company wanted to implement Industry 4.0 for better production efficiency. However, it soon realised it was not possible with ad-hoc legacy network fixes. Thus, in 2022, it decided to switch to a private mobile network, spanning multiple factory sites across a large geographical area. Wi-Fi could not be used as it presents challenges in an environment with a lot of metal: metal reflects, refracts or even absorbs wi-fi signals due to their frequency. The private mobile network not only helped the company mitigate Wi-Fi challenges but also provided other advantages such as:

• Dedicated and secure 4G connectivity for all British Sugar manufacturing facilities as part of a major ‘‘factories of the future’’ upgrade. Flexible and controlled mobile broadband connectivity in a complex factory setting with lots of metal.
• Facilitation of next-generation manufacturing techniques at its sites, including automated production lines, enhanced worker safety solutions, mobile asset tracking, autonomous guided vehicles and connected drones that can monitor tall structures such as silos and lime kilns remotely. This measures areas aimed at increasing productivity, boosting efficiency and improving health and safety on site.
• A future-ready network that is quickly upgradable to 5G. This will help British Sugar benefit from the higher speeds and lower latency of 5G while fully embracing the Industry 4.0 ecosystem, with more than 15 different digital manufacturing use cases in plan.

This approach of having a unified network strategy and dedicated investments in a modern network will result in several benefits, creating a significant impact across the enterprise.

First, reallocating budgets towards network modernisation reduces capital and legacy network operational costs. Continuing to invest in the maintenance of legacy networks, on the other hand, leads to a downward spiral of technology debt, limiting innovation and causing ad-hoc issues and security holes, thereby escalating overall legacy network costs. Investing in modernisation results in high-performing networks, which eventually reduces the resources tied up in managing tactical issues arising from legacy networks.

Our econometric analysis indicates that companies that shift spending away from maintenance of legacy systems see a decrease in their total annual network spend over the long run. Our modelling shows that a 30 percentage-point shift in spending away from the maintenance of legacy systems towards network modernisation over three years can help a typical company reduce its annual network spend by up to 21%. The specific reduction varies by industry, ranging from 12% to 21%, depending on the dynamics of capital and operational costs for each vertical industry.

Secondly, the right network modernisation investments can drive innovation-led growth in the future. We found that companies that shifted their network spend towards modernisation by 50 or more percentage points are 2.4 times more likely to be top industry spenders on innovation than their peers — the top 25% in an industry in technological innovation spend-to-revenues ratio. In essence, companies that shift towards modernisation are more likely to be the top innovators amongst their industry peers, assuming a top industry spender in innovation is a proxy for a top innovator.

While flipping the budget, enterprises need to align their strategy, technology and talent imperatives as well to transform their network from a position of tech debt to competitive advantage:

1. Sync the network strategy with the C-suite agenda: The first step in modern network planning is to align a company’s network strategy with the overall vision of the business; network can be the anchor that helps companies achieve their business objectives.
2. Create an elastic, configurable and consumable cloud-first network infrastructure: Software-defined, cloud-based architecture is the foundation of a modern network, as it enables flexibility and agility in network infrastructure and helps manage connectivity across multiple clouds. Automation and security are built on top of this cloud-first network infrastructure with unique benefits.
3. Build future network talent and operational model enablers: For the full value of network transformation to be accomplished, modern network technical enablers should be closely intertwined with appropriate operating models and process transformation. It also requires a workforce equipped with modern network engineering skills either through training or by working with partners.

While the challenges surrounding network transformation may appear formidable, companies can progress on the network maturity journey by shifting budgets towards network modernisation and focusing on these three imperatives. A modern network will help businesses forge a path that will not only reduce capital and operational costs but also improve business resiliency and help them drive innovation-led growth in the future.

The authors thank Ramani Moses and Taniya Chandra from Accenture Research for their contributions to this article.

About the Authors

Jefferson Wang is the lead author of the best-selling book, “The Future Home in the 5G Era.” He is Accenture’s Cloud First Global Technology Convergence Lead, focused on the harmonization of modern networks, edge, cloud, data, AI and security to create new user experiences and reinvent the back office. Jefferson is a regular speaker at Mobile World Congress (MWC) and Consumer Electronics Show (CES) and has appeared on CNN, CBS, NBC, ABC and Mobile World Live TV. His perspectives are featured in publications, including The Wall Street Journal, USA Today, Forbes, Fortune, New York Times and Washington Post.

Michele Marrone is Accenture’s market lead for the Europe Cloud First Network organization which equips our cross-industry clients with next-generation products and services, including technologies like 5G, software-defined networking (SDN), network function virtualization (NFV), Edge Computing and Media Cloud—all which key enablers of the hyperscale revolution. He is part of the Europe Executive Committee for Communications, Media & Technology and ICEG Executive Committee. He was also in charge of the Mobility Managed Services Unit start-up within the Accenture New Business organization.

In her role as research lead for the communications and media industry, Swati Vyas drives research and thought leadership across a number of topics in the industry. Having spent over 14 years in research, Swati has skills in thought leadership, next generation networks research, communications and media industry research, technology research, financial analysis and econometric value analysis. She has co-authored various Accenture thought leadership pieces. Two of her recent publications are “Modern networks: How to fast track competitive advantage” and “Start at the center: network-led transformation for growth.”

Reference:

1. Modern networks: How to fast track competitive advantage, 2023, Accenture, https://www.accenture.com/ch-en/insights/cloud/modern-network
   

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The reason behind such a major increase in value has to do with early adoption. Companies understand the importance of fast data processing and comprehensive analytics, which is why they’re not afraid to invest in various solutions regardless of the price.

Another major factor is the rise of AI and IoT. The increased popularity of artificial intelligence and Internet of Things devices will boost the demand for chart database solutions. Furthermore, as many companies become reliant on machine learning insights, they will need to invest in graph databases to stay on top of the competition.

In this article, we’ll analyze the market and its growth around the world.

Understanding graph database market

As the name indicates, graph databases are a type of databases heavily reliant on graphs. Given that graph structure offers great performance, scalability, and flexibility, it is a perfect method for storing data. By using products like NebulaGraph graph database, businesses can store their data and quickly retrieve it at any time.

Market drivers

There are several good reasons why these solutions have exploded in the last few years:

• Increased reliance on database tech

Companies that are in the manufacturing business are major users of graph database products. These solutions are vital for their data management, allowing them to oversee complex operational processes and transactions.

Compared to other types of systems, graph database tech provides numerous benefits for all commercial entities. They excel at problem-solving, which commonly occurs when analyzing large and complex data. With this technology, businesses can process large quantities of information in a minimal amount of time.

• Processing queries with low-latency

Due to the fact that many companies are turning to graph databases, legacy database providers are given the hard task of assimilating schemas into their existing infrastructure. While this strategy might seem a good way to save money, it will actually reduce query performance that runs beside the database.

By relying on graph database products, companies can change how they manage traditional trading activity. The increased demand for products that can deal with low-latency queries is another major reason why this market has been booming in the last few years.

Market challenges

The biggest issue with graph databases comes in the form of standardization. We also can’t neglect complex programming requirements.

The thing about graph databases is that companies need to run them on a single server. The reason behind this is that you can’t distribute such a technology in a low-cost cluster. As a result, you can expect a rapid decline in network performance.

Another major issue with this type of database is that programmers are forced to create inquiries in Java. Basically, graph databases are NoSQL, which prevents data saving. As a result, IT companies need to hire veteran developers, all of which come with a high price tag. Subsequently, the market is being developed much slower than some companies would like.

Graph database strategy

Staying on top of the current trends is the best way to maximize graph database technology. By implementing these solutions to your operational workflow, companies can retain a competitive edge regardless of their specific market situation. Among others, through graph database products, businesses can become more efficient and cost-effective while also gaining a tech edge.

Besides improving their strategic decision-making, implementing graph databases can have a major impact on customer experience. Without relying on data, businesses can easily lose their spot on the market, miss on potential revenue streams, or fall behind in manufacturing.

The best way for a brand to optimize its graph database technology is by attending live events and tradeshows, learning about current trends and technology, and performing thorough research. Keeping in touch with influential IT experts can also teach you how to best implement these solutions for your workflows.

Graph database regional analysis

Generally speaking, the growth of the graph database market is expected across the board. Although the US is leading the charge, powerful Asian economies aren’t lagging behind.

In North America, the rise of the graph database market will be propelled by increased reliance on high tech. Whether we’re talking about digital companies or anyone else that profits from these solutions, we’ll see the biggest adoption among brands that are heavily dependent on data analytics for their strategic and daily tasks.

A similar thing can be said for Asia Pacific. As IoT devices become more popular in the region, the graph database market will catch up with the increased demand. The technology is crucial for business owners, providing them with valuable information that will allow them to stay ahead of competitors.

On the other hand, the reason why the graph database market is growing in Africa and the Middle East has to do with data visualization software. On top of that, there are numerous governmental projects that are reliant on these products. As for Europe, the market growth stems from increased smart technology adoption.

Best graph databases in 2023

Graph database business is relatively new and dynamic. New names hit the market every month, each presenting businesses with innovative solutions to their daily problems. In 2023, some of the best graph databases are as follows:

• NebulaGraph
• IBM Cloud Databases
• Redis
• Stardog
• TigerGraph
• Apache Cassandra
• Virtuoso
• Fauna
• Infinite Graph

Choosing the right product isn’t easy. There are so many things that a company needs to consider, including:

• Scalability and performance
• Highly available solutions that can survive several system failures
• ACID compliance (Atomicity, Consistency, Isolation, and Durability)
• HTAP, OLAP, and OLTP application support
• Advanced graph data science
• Graph query languages
• Native graph processing and storage
• Open source foundation
• Community and many other factors

Of course, every business will have its specific requirements. Only by finding the right combination of features can you be certain that a product is suitable for your business needs.

The post Graph Database Market Overview appeared first on The European Business Review.

The Power of Data in Stokehold Management

Data is the new currency in the maritime industry, and its value extends to the heart of a ship—the stokehold. Modern vessels are equipped with a plethora of sensors and monitoring devices that continuously collect data on various parameters, from engine performance and fuel consumption to temperature and pressure levels. Harnessing this wealth of data is the cornerstone of data analytics in stokehold management.

Predictive Maintenance: Anticipating Issues Before They Occur

Predictive maintenance, a subset of data analytics, involves the use of advanced algorithms and machine learning models to predict potential failures or performance degradation in stokehold equipment. By analyzing historical data and real-time information, these systems can forecast when specific components might require attention, allowing for effective stakeholder management.

Minimizing Downtime and Maximizing Efficiency

One of the primary benefits of predictive maintenance in stokehold management is the significant reduction in unplanned downtime. By identifying potential issues before they escalate, maritime operators can schedule maintenance activities during planned stops, minimizing disruptions to vessel operations. This proactive approach enhances overall efficiency and reduces the financial impact of unexpected breakdowns.

Cost Savings and Operational Efficiency

The financial implications of stokehold failures extend beyond immediate repair costs. Unplanned downtime results in revenue loss, increased operational expenses, and potential reputational damage. Predictive maintenance not only reduces these costs but also optimizes the use of resources by directing efforts precisely where they are needed, ensuring that maintenance activities align with the actual condition of equipment. 

Safety Enhancement for Crew and Vessel

Predictive maintenance contributes significantly to safety in maritime operations. By addressing potential issues in advance, the risk of critical failures is mitigated, reducing the likelihood of accidents or emergencies in the stokehold. This approach enhances the overall safety of the vessel and safeguards the well-being of the crew members working in close proximity to the stokehold.

Real-Time Monitoring and Decision-Making

Data analytics in stokehold management goes beyond predicting maintenance needs; it also enables real-time monitoring of equipment and systems. Through connected sensors and monitoring devices, maritime operators can receive instant updates on the condition of critical components, allowing for informed and timely decision-making to optimize performance and address emerging issues.

Tailoring Maintenance to Specific Needs

Condition-based monitoring is a key aspect of data analytics in stokehold management. Instead of adhering to fixed maintenance schedules, condition-based monitoring tailors maintenance activities to the actual condition of equipment. This approach ensures that resources are allocated efficiently, with maintenance efforts focused on components that genuinely require attention.

Integration with Fleet Management Systems

Data analytics in stokehold management is often integrated into broader fleet management systems. This interconnected approach allows for a holistic view of the entire maritime fleet, enabling operators to identify trends, benchmark performance, and implement best practices across multiple vessels. The synergy between stokehold-specific analytics and fleet management enhances strategic decision-making for the entire maritime enterprise.

Data Security and Skill Gaps

The adoption of data analytics in stokehold management comes with its challenges. Ensuring the security of sensitive data is paramount, given the potential cybersecurity threats in the maritime industry. Additionally, addressing skill gaps by providing training for crew members and maritime professionals on data analytics tools and technologies is crucial for the successful implementation of these advanced systems.

The Future Landscape of Stokehold Management

As technology continues to advance, the future of stokehold management holds even more exciting possibilities. The integration of artificial intelligence, advanced robotics, and improved connectivity will further enhance the capabilities of data analytics and predictive maintenance. The maritime industry is poised to leverage these innovations to create smarter, more efficient, and safer stokehold management practices.

Conclusion

In the maritime industry, where precision, efficiency, and safety are paramount, data analytics and predictive maintenance in stokehold management represent a paradigm shift. These technologies empower maritime operators to navigate the seas with unprecedented insights, proactively addressing challenges and optimizing the performance of the critical systems that power the vessel. As the maritime sector continues to embrace the data-driven future, stokehold management stands at the forefront of innovation, ensuring smoother and more reliable journeys on the high seas.

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By 2026, global spending on digital transformation will reach upwards of $3.4 trillion, demonstrating how central the technological capacities of organizations have become. The reasoning behind this is simple: companies that employ data-driven decision-making are more profitable compared to companies that don’t.

Yet, to make use of company data to its fullest extent, businesses need to ensure that they have effective methods of data protection. Depending on the sector a business works in, data protection could pose more of a comprehensive challenge, with certain fields like healthcare requiring another layer of sensitive data protection.

In this article, we’ll explore data protection in business environments, demonstrating the leading strategies that companies can employ to keep customer and business data as secure as possible.

What is data protection?

Data protection is an umbrella term that covers internal business processes that help to reduce the likelihood of data breaches, unauthorized access to data, and the loss of company information. When ensuring that data is adequately protected, different industries will also have a range of compliance initiatives to follow.

The vast majority of companies will have to follow the local data protection legislation of their area. For companies in Europe, the General Data Protection Regulation (GDPR) framework outlines core practices they must abide by and instill in their business. Different regions will have a main dominating data protection regulation.

Alongside regional compliance, businesses in certain fields will have to follow industry-based compliance regulations for their data. These regulations could range from ensuring that businesses have effective authorization protocols in place to providing proof of a comprehensive cybersecurity solution.

What are the key methods of data protection in business environments?

With how expansive data protection is, it’s hard to know exactly where to start. For newer businesses, beginning to implement a compliance framework as extensive as the GDPR can pose a challenge.

To get a running start, here are some core data protection methods that your business can include:

Backups and data recovery initiatives

The single best tool a business can count on in the face of a data disaster event is a healthy and accessible backup. Backups provide a fail-safe copy of data that companies can utilize and restore if anything goes wrong with their current system. For example, if a business falls into a ransomware scheme, they have another option that allows them to continue working rather than only having the option of bargaining with the malicious actors.

Equally, backups prevent data loss on a massive scale. We all assume that we’ll never have to experience a sudden and seemingly random data loss event. However, they’re more common than one might think, making having a backup to fall back on more important than ever.

There’s a reason that 90% of companies back up their data – this is a vital step when ensuring the overall protection of data.

Authorization and Access Control

Authorization and access control are two of the most fundamental approaches when providing a secure system for data storage. Access control is the process of assigning every user in your system with a specific role or level within your business. Depending on their role, they will have access to a different number of files or areas of files.

For example, a network administrator may have access to every single document that a business stores in its data warehouse, while a member of the sales team might only have access to sales-related documents and processes. This distinction ensures that if a malicious actor were to gain access to a user account, they wouldn’t be able to harvest all of the data from your system, being limited by the associated role of the account.

Another effective layer to add to this solution is authorization. Authorization mechanisms will ask users to prove their identities when logging into your company accounts. Most of the time, this process will use Multi-Factor Authentication (MFA), which asks a user to confirm their identity on their mobile phone or another connected device.

MFA helps to reduce the likelihood of an attacker gaining entry into your system. Although they may harvest account and password details, they won’t be able to go any further as they don’t have access to a second device.

Authorization and access control systems are a wonderful addition to any secure data storage environment.

Encryption

Encryption is one of the most important elements of effective data protection strategies as it ensures that data is unreadable to anyone who may intercept it. Encryption encodes data, only revealing the data to parties with the correct decryption key. These keys are automatically passed between your end-to-end systems, allowing businesses a high level of security without the hassle of needing to wait for decryption.

Yet, if your data is involved in a breach or intercepted by a third party, then it will simply appear as ciphertext, a long string of random characters that is nearly impossible to decipher. By ensuring that you have encryption protocols in all of your data storage facilities, you will simultaneously meet the requirements of the GDPR and other compliance bodies while also making your data harder to access for unauthorized parties.

Encryption is a must-have in the world of data protection.

Final Thoughts

If a business wants to reap the benefits from data collection, processing, and analytics, then it must also be prepared to take on the extensive data protection strategies and compliance frameworks in place. These recommendations and requirements help businesses to protect customer data and avoid the potentiality of a data breach.

Considering that losing customer data and being involved in a data breach can have significant consequences on revenue, brand trust, and reputation, it’s always a good idea to make data protection a top organizational priority

The post Exploring Key Methods of Data Protection in Business Environments appeared first on The European Business Review.

In a digital age characterized by the exponential growth of data, the European Union is taking a significant step towards harnessing the power of information with the European Data Governance Act. This legislation, which came into force on June 23, 2022, and became applicable in September 2023 following a 15-month grace period, holds the promise of bringing substantial benefits to both EU citizens and businesses.

Building Trust and Breaking Down Barriers

At its core, the Data Governance Act is designed to foster trust in data sharing while addressing the technical challenges that often hinder its reuse. This initiative is pivotal in the broader European strategy for data, which seeks to facilitate data availability and enhance cross-sector and cross-border data sharing.

One of the central objectives of the Act is the establishment and advancement of common European data spaces in key domains such as health, environment, energy, agriculture, mobility, finance, manufacturing, public administration, and skills. These data spaces will involve collaboration between private and public entities and pave the way for innovation and progress in these vital sectors.

The Benefits of Data Governance

The benefits of this legislation are far-reaching and encompass various aspects of our lives:

1. Fostering Innovation: Proper data management and sharing will serve as a catalyst for industries to develop innovative products and services. It is the lifeblood of training AI systems, enabling them to make smarter decisions.
2. Enhanced Governance: More accessible data will empower the public sector to craft better policies, leading to transparent governance and more efficient public services.
3. Data-Driven Solutions: Data-driven innovation holds the promise of transforming our lives and work, making them more efficient and sustainable. This includes improving personalized healthcare, saving billions in the EU health sector, enhancing mobility through real-time navigation, combating climate change with environmental data, revolutionizing agriculture with precision farming, and improving public administration through reliable official statistics.

The Practical Implementation

The EU will implement the Data Governance Act through a set of four comprehensive measures:

1. Reusing Public Sector Data: Certain public sector data that cannot be open will be made available for reuse, particularly in critical areas like healthcare research.
2. Trustworthy Data Intermediaries: Data intermediaries will be entrusted with the responsibility of ensuring trustworthy data sharing within European data spaces.
3. Empowering Citizens and Businesses: Measures will be put in place to make it easier for individuals and companies to contribute their data for the greater good.
4. Facilitating Cross-Sector Data Sharing: This entails streamlining data sharing processes across sectors and borders, ensuring the right data is accessible for the right purpose.

A Catalyst for Innovation and Progress

The Data Governance Act isn’t just about data; it’s about fostering innovation, creating jobs, and addressing pressing societal challenges like climate change and global health crises. It will drive down costs for acquiring, integrating, and processing data, lowering barriers to market entry. Both small and large enterprises will have the opportunity to develop data-driven products and services, stimulating economic growth and job creation.

As we move forward in the digital era, the European Data Governance Act stands as a testament to the EU’s commitment to ensuring that data becomes a force for good, benefiting society as a whole while fostering economic growth and innovation. It’s a bold step towards unlocking the potential of data and building a brighter future for all.

About the Author

Andrea Maura is an Aliant Lawyer from Aliant Legal Grounds Italy.  He is active as a litigator primarily in insurance cases, and he is often a speaker and a trainer at conferences and workshops held by insurance companies, intermediaries, and associations. Besides his participation to numerous collective operas, and publishing on legal magazines in the insurance sector, Andrea has published three books as a solo author: “The civil and penal responsibility of the directors of incorporated companies”, (Halley 2007), “Damaging party, damaged party, and automobile liability insurance”, (Maggioli 2011) and “Damages liquidation in automobile liability insurance”, (Maggioli 2014).

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The journey towards the decarbonisation of supply chains requires harmonised and reliable information on emissions as well as industry collaborations by not-for-profit organisations operating safe and trustworthy data-sharing initiatives.

Decarbonisation of supply chains: why data sharing is vital

As the decarbonisation of supply chains is becoming imperative, companies need standards for calculating and reporting the greenhouse gas (GHG) emissions of their product. Over time, suppliers along complete supply chains will share their calculations with their clients and other stakeholders. Fortunately, emissions can be quantified as carbon-emission equivalents, simplifying the measurement process – hence the term carbon footprint. However, today, we are far from having access to harmonised, detailed and reliable information on emissions. While some generic guidelines such as the Greenhouse Gas Protocol exist, consistent industry approaches at product level are lacking. Achieving this will require collaboration along supply chains in the years to come; undertaking this collaborative effort is vital for several reasons. First, consistency and comparability are key to understanding the relative footprint of different raw materials, components, or products. Having estimates at the company level is not enough to decarbonise our supply chains. Second, solid data foundations help understand emission drivers and initiate incremental and radical improvement projects. In a nutshell, to find alternative raw materials and design more circular supply chains, companies need data they can trust. Finally, product carbon footprint transparency is important for stakeholders and policymakers who expect companies to fulfil their climate commitments and targets.

In the future, achieving climate change ambitions will require even more data sharing between companies and digital solutions that span entire supply chains. Beyond the sharing of product-specific greenhouse gas (GHG) emissions, EU regulations are pushing companies to share information that facilitate the adoption and scaling of circular economic models. One EU initiative focuses on batteries. With the rise of vehicle electrification, demand for battery raw materials will increase rapidly. To achieve a sustainable transition, a circular approach is necessary to ensure sustainable material sourcing, efficient battery production, and effective end-of-life processing that maximise the reuse of materials. By doing this, the whole industry will reduce emissions and reduce its dependencies on certain raw material sources.

In this context, EU Regulation will soon require data-digital product passports from companies along the battery supply chains – these passports will contain information about the battery’s composition, environmental impacts, and end-of-life procedures. This data-sharing scheme is designed to provide transparency and accountability throughout the battery’s life cycle, from production to disposal. Companies such as BASF, Umicore and BMW have taken the lead in creating such a passport.

In this context, no company will be able to make information exchange on emissions and other sustainability factors happen on its own. We will need industry collaborations orchestrated by not-for-profit organisations that operate safe and trustworthy data-sharing initiatives. In this regard, Together for Sustainability (TfS) is a role model in the chemical sector that can inspire leaders across industries. In the present article, we review the history of TfS, describe its current product carbon footprint initiative, and discuss how its model could be mainstreamed across different industries.

Together for Sustainability

TfS is a global sustainability initiative for the chemical industry. It was founded in 2011 by six leading chemical companies: BASF, Bayer, Evonik, Henkel, Lanxess, and Solvay. TfS aims to improve sustainability practices in the chemical industry supply chain through data-sharing initiatives. It also provides training and support to suppliers so they can improve their sustainability practices. Today TfS has 47 members whose aggregated revenue exceeds $800 billion; this not-for-profit foundation brings together more than 14,000 suppliers along a common improvement path.

How TfS started

Back in 2011, supply chain due diligence had emerged as a critical issue for businesses. The idea for TfS emerged in 2011 when six Chief Procurement Officers (CPOs) from the chemical industry came together to address a common problem. They realised that approaching suppliers separately with different standards and questions would prevent these suppliers from concentrating their efforts on progress. To solve this problem, they decided to create a standard that would reflect the needs of the industry. Their motto was simple: ‘‘An audit for one is an audit for all.’’

A virtuous change cycle fuelled by trust.

Over the years, TfS has evolved into a professional not-for-profit organisation that orchestrates a virtuous change cycle. As TfS onboards new members, a broader pool of suppliers and data is created. As the pool of data broadens, more members see the benefit of joining TfS. Such a virtuous change cycle was possible because TfS created the conditions for industrywide trust for TfS members and for their suppliers.

On the member side, trust was established through three means: responsibility, exemplarity, and compliance with antitrust laws. First, companies that join TfS must be represented by their CPO.

With this decision-making power, each member dedicates the resources needed to support the TfS workstreams. Furthermore, no members can get a free ride, all of them commit every year, to bring their share of suppliers into the pool. TfS has a dashboard that outlines the number of suppliers involved, the aggregated scores of suppliers, and the number of corrective actions closed across the industry. This creates a sense of responsibility and instils emulation. Second, over the years TfS has focused on quality over quantity. To become a member, a company needs to score high against TfS’s own criterion so they can lead by example. By doing so, members also have a clear understanding of the stakes and efforts required to spearhead sustainability improvement. Third, the TfS initiative is antitrust compliant: companies share data without knowing with whom a supplier works. This is an important foundation that requires a professional organisation and solid governance, so everyone is confident.

On the supplier side, suppliers also gain benefits beyond the elimination of paperwork. TfS helps them save time but also gain access to resources that help them improve. While members benefit from experience and best-practice sharing, TfS also offers capability-building initiatives to suppliers who gain help from members. This allows them to develop their capabilities, image, and reputation. As suppliers own their assessment and audit results, they can use them to gain credibility in the market and to support their commercial efforts. Finally, TfS has established partnerships with diverse industry associations and has developed active connections across the world, creating an expanding and supportive ecosystem.

Democratising carbon footprint data

As members of Together for Sustainability started to commit to reducing their greenhouse gas emissions, they realised the importance of calculating the carbon footprint of their products in a consistent way. TfS consequently launched its Product Carbon Footprint (PCF) Guideline in September 2022. The guideline is open source, available in five languages, and accessible to everyone. The guideline provides a standard for calculating the carbon footprint of chemical materials. TfS is now piloting a digital solution that enables corporations and suppliers to share upstream product carbon footprints and manage their emissions of purchased goods and services. In a nutshell, TfS promotes the democratisation of product carbon footprint measure, and it offers a safe digital space to share such information.

With the TfS guideline and the associated digital platform, the data used by R&D, sales and procurement across the chemical industry can progressively be unified, making the development of competing methods of calculations for the carbon footprint of chemicals superfluous. As this occurs, providing information on product carbon footprint to clients will soon be regarded as a basic service and sharing such information on a broader scale needs to rapidly become one of the foundations for more systemic changes that will help fight climate change.

Measurement is a necessity, but it will not reduce emissions by itself, companies will need to turn measurement into priorities and priorities into results. No company should wait for perfection in measurement and reporting to start acting! But sound and rapid progress can only be achieved if we reduce the time it takes to have a common and recognised standard.

To help visualise this trust and measurement challenge, Table 1 can help decision makers assess the maturity of a company’s approach on measuring scope 3 emissions. This table is not part of the TfS guideline, it applies to any situation where a company depends on suppliers to measure its scope 3 emissions. Thanks to the TfS Product Carbon Footprint Guideline, we can rapidly progress from level 0 to level 2, so emissions are calculated and shared by suppliers based on sound industry guidelines and methods. Then, we will need to progress to level 4 where comparable, detailed, and reliable information on emissions exists across industries and along the supply chains. Some large companies already have the capabilities needed to reach level 4 while others are earlier on in their journey. Through TfS they can share their expertise and practices so everyone in the industry can reach level 4.

Gaining trust in the emission shared across companies cannot be achieved without widely accepted standards and guidelines. No company should spend time re-inventing the wheel on its own. Competing initiatives should not flourish. With sound collective efforts within an industry, we can reach level 4 in five years, with fragmentation and competing standards, it will likely take 15 years to get there.
Other industries should adopt the TfS model!

Over time, it is important to democratise access to data on GHG emissions to ensure everyone understands the impact of their actions on the environment. This data should be used to inform policy decisions, create awareness, and help individuals and organisations reduce all their emissions. Additionally, having access to data helps ensure that all stakeholders are held accountable for their actions and have the necessary information to make informed decisions.

In this context, other industries can build on the efforts undertaken by TfS. Establishing organisations such as TfS is critical. Professional and trusted not-for-profit foundations facilitate safe data exchange and help extract commercial sensitivity out of them. Three important lessons learned from TfS can be outlined to increase the chance of success for such an initiative.

First, it is of utmost importance to have a dedicated professional organisation. Orchestrating data sharing requires establishing professional not-for-profit foundations. Only not-for-profit foundations can rapidly rise as a trusted body; they can ensure that diverse regulations including antitrust laws are respected, and that potentially sensitive data is protected. Such not-for-profit organisations are best served by a transparent governance process where decisions are documented and widely available. Decisions need to be taken collectively within key projects, endorsed by all members. Good governance also requires an active board that mainly sets the ambitions, defines the functioning of the organisation, and steers the direction of the organisation.

Second, such initiatives need to balance short- and long-term benefits for participants. In the case of TfS, all participants benefit, right from the start, from the elimination of excessive paperwork, the availability of a data-sharing infrastructure, and access to knowledge. However, over time, participants can gain further benefits both at the company level and at the industry level. They can strengthen their credibility in the market and take advantage of a more transparent and sustainable supply chain.

Third, such initiatives need to create a positive ecosystem momentum and mobilise a broad array of members. A not-for-profit foundation that manages a data-sharing initiative plays a pivotal role in attracting new members, but it should also set the bar high for participants. It needs to ensure that members act responsibly, contribute to the collective efforts, and remain exemplary. Members’ contributions and participation should be visible and publicly recognised. At the same time, the not-for-profit foundation that supports data-sharing initiatives should ensure that only exemplary members can join and that all members contribute a fair share of resources to the collective effort. The initiative will be successful if a critical mass of industry players can be rapidly created to instil a virtuous circle of mobilisation and progress.

Conclusion

At a time when we need solid data foundations for the decarbonisation of supply chains, TfS is a prime example of how collaboration can enable the transformation of industries. TfS has created a standard that serves the chemical industry well; the organisation is also ready to support other industries. We need to promote and progress towards the democratisation of product carbon footprint data, which will help create actionable insights and drive progress. Some of the TfS members even believe that they have a responsibility to help other industries fast-track their development. Our collective stake is to create standards that are available for free, avoiding total fragmentation in the way product carbon footprint is measured. Let’s make it happen fast!

About the Author

Hervé Legenvre is a Professor and Research Director at EIPM. He manages educational programmes for global clients. He conducts research and teaches on digitalisation, innovation, and supply chain. Lately, Hervé has conducted extensive research on how open-source software and open hardware are transforming industry foundations. (www.eipm.org)

The post Building the Data Foundations of Supply Chain Decarbonisation appeared first on The European Business Review.

Managing risk in the early days

Data analytics is useful in managing risk because it enables a new business to learn more about the areas where they are weak and when action needs to be taken to fix these problems. Companies can experience losses of productivity or revenue as a result of many things, from operational inconsistencies to a lack of employee skills and unhelpful marketing. By pinpointing these concerns, it is possible to come up with improvements, either through retraining and restructuring or through evaluating the costs of each process. Using data analytics, business leaders can gain insights into their start-up, and then use this information to address any shortcomings before they become too costly.

What is data analytics?

Most people with an interest in business are likely to have heard of data analytics. It’s made up of many different technologies, devices, and practices, all of which are employed to discover trends and manage problems using data. This data is collected through surveys, tracking consumer behavior on the internet or through purchasing datasets from specialists.

Once they have this raw data, analysts clean it to remove errors, atypical points, and duplicate information. This results in better quality data that will produce more precise results during modeling. Once the data is structured and filtered to learn more about how the different areas relate to one another, it can be interpreted. Here, analysts search for patterns in the data and trends which can reveal the information a business needs in order to answer any questions which have been asked. Finally, they will present their findings in a way that is accessible to the rest of the team. They might use something as familiar as an Excel spreadsheet, or they might create written reports to elaborate on their conclusions.

Business analytics is crucially important to modern organizations, and people who can interpret data are always in demand. They help to nurture a business’s success by improving decision-making and informing its processes. People who are planning a career in this field can accelerate their learning with the online business analytics programs at St. Bonaventure University. The online master’s in business Analytics is an accessible course that’s delivered over two years. It gives students the knowledge they need to develop their problem-solving skills and become confident in the use of technology.

Why is data analytics important?

Business people might feel confident in acting on a hunch or using their intuition. For some, this second sense, often informed by practical experience, can give them the edge. However, it can also be considered a nebulous method of decision-making, especially for vulnerable start-ups. Instead of relying on their gut, business leaders can also use metrics to keep their endeavors logical and based on fact.

As so much data is harvested and condensed to form even the smallest of insights, this process gives companies more clarity when it comes to the processes they employ and the service they provide. It allows them to see the business from the customer’s point of view, whether they are finding the solutions they need on the website or are experiencing problems, for instance. By connecting their discoveries with actionable ideas, companies can offer user-friendly websites, save money on their manufacturing processes and boost workplace productivity.

Cutting costs for new businesses  

Big data technologies can inform a range of processes that occur when any new business is launched. This includes developing new products, manufacturing them efficiently, and delivering the product or service. Understanding more about the data that is being produced can help a business to keep its customers loyal, retain its employees, and improve the logistics side of things.

Avoiding a high turnover of employees

Once a business has a good team that includes people who get along well, know their job, and are reliable, it makes sense to keep them for as long as possible. When employees leave to work elsewhere, it can be costly in terms of recruitment and retraining, but that’s just the tip of the iceberg. Data analytics can inform companies at the very start by picking out the best people based on the information they have given about themselves, and the results of surveys given to them as potential candidates.

Analytics can also be used to learn more about which aspects of the job are challenging for employees. Companies that show they are willing to help and eager to improve employee satisfaction are more likely to have a loyal workforce. This information might be gleaned from surveys that collate the responses which have been gathered into a graph, so a business can identify which factors are of most concern. Then they can consider what action might alleviate these key problems.

Attracting and retaining a client base

By analyzing the data that has been gathered and collated, start-ups can understand more about their buyers and learn how to keep their customers. Customer retention analytics is a metric all of its own; the data produced shows how satisfied customers are and what can be done to keep them feeling that way. Information is gathered about what customers buy, when they buy it, and whether they choose to buy it again. If they returned an item or stopped using the service, that action will also be taken into account. Even in the earliest days, measuring the journey of buyers can be useful to businesses. It allows them to understand what is going well and what leads to the loss of a customer.

Giving productivity a boost without micromanaging

All businesses want to keep their teams productive, but constant reviews and monitoring can make people feel uncomfortable. Big data can use tracking tools to automate processes that employees usually do, such as logging in or out deliveries, so people have more time for their other tasks. Furthermore, this technology uses digital records to track goods as they move around a facility, so people can find them swiftly. When people are free to use their skills and work creatively, they are likely to be satisfied in their roles and be more productive. Furthermore, businesses can use data analytics to identify single processes that can be automated, such as choosing shipping methods or routes. These real-time decisions can take pressure off the team, reduce costs and ensuring customers are happy with the service.

Developing products and services that customers need

Most start-ups begin with a limited number of products or services, but they are keen to grow this portfolio as soon as it is viable to do so. Data analytics is useful when a business is developing a new product or service, as it offers a method of gauging customer behavior and preferences. It can be useful to look at transactional data from a broad range of sources rather than concentrating on the information produced by the business itself. This can reveal not just what people are buying but the amount they are willing to pay. A start-up can utilize this data to design desirable products, set an optimal price point, and feel confident in how much they might sell.

Marketing campaigns that adapt to consumer preferences 

Consumer behavior insights provide information on what and when people buy and how they prefer to buy. For instance, a company might discover that sales went up after a video was added to its YouTube account. That would indicate buyers were influenced by social media and possibly other online channels. As a result, the marketing team will reallocate their budget accordingly, and the company saves money by concentrating on the most effective marketing methods. The additional funds can be diverted elsewhere to keep growing the start-up and making improvements. This level of personalization can be translated into other areas of customer interaction using big data. To boost engagement, a start-up might add a person’s name to their emails or texts or create a tailor-made catalogue of recommendations based on their browsing, both of which enhance the overall customer experience. 

Monitoring the performance of a business

Start-ups need a way of managing their performance if they are to succeed in a competitive environment. A dashboard which reveals all the most important indicators acts like an early warning system when it comes to identifying problems at an early stage. This allows professionals to see where adjustments have to be made and keeps a business moving in the right direction. Moreover, it ensures that the company’s leadership team, the employees, the assets and the processes used are all aligned in a common purpose. Most young businesses will inspect their finance sheets regularly and in great detail, but often it takes time for bottlenecks or dips in performance to be recognized this way. This is particularly true when monthly reviews are relied upon. Analytics offers a more dynamic way of tracking financial data using advanced reporting tools that create daily reports and highlight issues as they occur.

Using analytics to write a budget

Budgeting is essential for any size of business. Accurate financial data ensures that decisions are properly informed when it comes to expansions, taking on new staff and launching new products. It also makes it easier for a company to manage its debt, pay off loans, and obtain more generous levels of finance. Businesses use budgeting as a method of self-assessment; it allows them to make key decisions which bring them closer to their short and long-term aims. By using analytics to create a budget, companies get a bigger picture because the plan takes into account so much more data. It can offer insights into busier periods, seasonal trends, and buying patterns and then account for these in a comprehensive report.

Which data analytics innovations can deliver value for start-ups?  

Big data analytics is made up of several types of innovation; these resources work together to extract the most meaning from a company’s data. Here are some of the most groundbreaking analytical tools and a look at why they have value for new businesses.

Lowering costs with cloud analytics

Cloud computing and analytics involve using analytic algorithms to process data that is stored in a virtual environment. It is used to find new insights in many areas of industry, especially improving product delivery and availability as well as studying consumer behavior. In a world where huge amounts of data are collected, cloud analytics is seen as more accessible and user-friendly as it can perform complex tasks without the need for physical hardware. By reducing the need for infrastructure, cloud solutions allow businesses to reduce their costs and only pay for the services they need, as they need them.

Making insights sharable with data management programs

Collecting vast quantities of data is not useful in itself. New businesses need to govern their data well if they are to get the most out of it. Once a system of retrieval is set up, data will be constantly flowing into the company, so it’s important to consider how it will be managed. By investing in a solid data management program, companies ensure they can catalogue, search within, and ultimately make sense of data. Furthermore, when a program is accessible, all team members can find what they need and can ultimately do a better job. This could be in terms of boosting efficiency in part of the workflow or responding to changes in consumer demand.

Machine learning can forecast trends

Businesses which incorporate machine learning into their strategy at an early stage often see the benefits promptly. Machine learning uses AI to train machines to learn more quickly, process large amounts of data, and then deliver extremely detailed and accurate reports that keep a company ahead of the competition. Most streaming companies use machine learning to analyze the type of programs their viewers enjoy and then provide recommendations based on each person’s preferences. In other types of business, the same principle applies. Machine learning will model data analytics in a powerful way to forecast demand for certain products or services. This results in better inventory supervision, satisfied customers, and higher sales. Furthermore, signposting trends allows analysts to find potential opportunities more quickly and also to avoid unprofitable ventures.  

Predictive analytics aids the decision-making process

Predictive analytics combines several technologies including machine learning and statistical algorithms. By doing so, it is able to recognize the likelihood of an outcome based on past data. Start-ups may not have enough historical data in the first months, but as time goes on they will gather enough information for predictive modelling to be a success. Alternatively, they can use a platform or a service which has information and insights that are relevant to their business. Either way, predictive analytics can set in motion a period of constant growth. It allows companies to learn more about their customer’s needs and buying trends, which means they can make the best decisions about launching new products. Furthermore, it can inform a business’s marketing efforts, taking them out of a niche and moving them into the mainstream. It does this by providing accurate information on potential audiences and revealing new demographics which may not have been targeted in previous campaigns.

Access to new information with text mining

When people start a new business, they might be too busy to look through every comment on their social media pages and in their product reviews. Text mining can take care of this by using AI to search for patterns, repeated phrases, and more. It unearths valuable information in a range of everyday text documents, from customer feedback to chatbots, emails, and text messages sent by clients. Consumer sentiment can help a company to see issues that they had not previously noticed, both good and bad. They can use the information to understand more about how a product or service is being received and whether improvements are needed. Text mining is a swift process and therefore allows businesses to take action with equal speed.

Create a concrete plan for future success

Using data analytics in its various forms, start-ups can make smarter, better-informed decisions about their futures. From spotting trends to tackling problems and forming better relationships with their customers, metrics take away the guesswork. Interpreting the data effectively allows a new business to understand more about its progress and to set goals for improvement. This means the company is constantly learning, pushing onward, and readying itself for the future.

The post Why Should Start-Ups Use Data Analytics to Drive Their Business Forward? appeared first on The European Business Review.

There is no shortage of descriptors when it comes to unveiling the considerable importance of data in our societies. While some refer to it as the new black gold, this comparison is somewhat appropriate but not entirely accurate. Just as oil is vital for energy, data has become indispensable and inherent to the functioning of our digital and artificially intelligent economy. But unlike oil, which diminishes as it is used, data can be utilized and shared infinitely.

As odd as it may seem or appear, at the dawn of the 21st century, the entire world is undergoing one of its greatest societal transformations since the invention of currency, yet it is not truly regarded with the same level of significance.
Data is the world’s first-ever common currency. And like money, it plays and will play a fundamental role in the economy and society.

Data is a unit of measurement

As money serves as a standard of value, data serves as a unit of measurement for insights and business performance. As soon as companies began using databases to track their operations in the 60s and 70s, data became a unit of measurement. With the development of analytical tools in the 80s and 90s, companies began measuring their performances through data in much more sophisticated ways. This is particularly visible in sectors where energy data is used to monitor efficiency, forecast demand and optimise operational performance.

The ‘total quality management’ movement of the 80s required intensive use of data. Simultaneously, the development of systems such as integrated management software (ERP) enabled companies to track and measure aspects of their operations in unprecedented ways. Data now allows for unprecedented opportunities in capital funding, underscoring its transformative role as a pivotal asset in modern finance.
The most striking modern example is probably the rise of Silicon Valley big-tech companies.

These companies-built empires by measuring and analyzing user behaviors on a scale never seen before, making data not only a unit of measurement but also the very foundation of their business model.

Data is a medium of exchange

As currency facilitates transactions, data allows businesses to better understand their customers and tailor their offerings. It is exchanged between entities for various services, such as personalizing advertising. The concept of data as a medium of exchange dates to the advent of the first computer systems, but its widespread adoption and recognition truly took off with the emergence of the Internet and, more recently, the rise of e-commerce and online services in the 90s and 2000s. As more and more businesses began offering online services, they realized that the data generated by users was valuable for improving their services, creating new products, or selling it to third parties.

A prime example of this transformation is the ascent of the online data search economy. Each online search performed by a user provides information about user interests, behaviors, and desires deriving massive revenue from targeted advertising using users’ search data. Data has thus become a form of currency with which users “pay” for services.

Data is a store of value

As money retains its value, relevant and well-preserved data can offer long-term strategic benefits to a company, even years after its collection. Companies quickly understood that the information they collected about their users was valuable in and of itself, not only for improving their services but also as a source of revenue.

Customer data aids in understanding buying patterns, preferences, and habits to recommend products, leading to increased sales. Besides, just as money acts as a reserve of value, safeguarding wealth for future investments, data too holds intrinsic worth, anchoring the potential for innovation.

Without this reservoir of data, pioneering breakthroughs in AI technologies—enabling the development of systems from autonomous vehicles to smart healthcare diagnostics and real-time language translation—would remain beyond our grasp.
The recognition that data can be used as a store of value was a turning point, leading to the era of the so-called “Big Data” where companies of all sizes and from all sectors seek to capture, store, and analyze data in hopes of deriving future value from it.

Data is a representation of sovereignty

Owning and controlling one’s own data has become a vital component of digital sovereignty, just as having one’s own currency is a symbol of national sovereignty.
As nations have become aware of the strategic implications of data concerning its storage, cross-border transfer, and access by foreign governments, it has become integral to national sovereignty.

China is perhaps the most emblematic example of data as a representation of sovereignty. With the adoption of its cybersecurity law in 2017, China implemented strict data localization rules, demanding that “personal information and critical data” collected by core information infrastructure operators be stored within its borders.
Many other countries, from Russia to India, have since adopted similar rules, underscoring how possession, control, and access to data have become central in contemporary notions of national sovereignty.

Data is an economic policy instrument

As currency is regulated to influence the economy, data is used by governments and businesses to inform their decisions and strategies. Particularly with the rise of tech giants, governments quickly grasped the strategic importance of data for economic development, competition, and regulation.

With the introduction of the General Data Protection Regulation (GDPR) in 2018, the EU established strict rules on data collection, storage, and sharing, thereby recognizing not only its economic value but also its importance in terms of human rights and individual freedoms.

Discussions about competition, data monopolization, and the impact of tech giants on the digital economy are now at the heart of political and economic debates.
The use of data as an economic policy tool is also evident in the regulation of artificial intelligence, digital privacy standards, and antitrust measures against data monopolies.

Data is an element of credit facilitation

Currency allows for the granting of credits. Similarly, quality data can open opportunities for partnerships and funding for businesses. Data became a credit facilitation tool with the rise of financial technologies, or “fintech”, in the 2010s. The surge of peer-to-peer lending platforms and fintech companies that use advanced algorithms to assess creditworthiness based on a variety of data – from financial histories to online shopping habits – was the harbinger of this transformation.

China’s Ant Financial, the owner of Alipay, stands as an iconic example of this shift. With its “Zhima Credit” product (also known as “Sesame Credit”), Ant Financial offers a credit scoring system based on data analysis sourced from user activities on Alibaba Group’s platforms and other sources. This score can then be used to secure loans, rent apartments, and even for certain government services.

The use of data in this manner has revolutionized access to credit, particularly for individuals and small businesses who previously struggled to obtain loans due to a lack of traditional credit history.

Data is a foundation of the tax system

While currency is essential for tax collection, data is increasingly used to monitor tax compliance and prevent fraud. Data became foundational to the tax system as governments began using digital technology to collect, process, and analyze tax information. This shift also gained momentum in the early 2000s, with the increasing digitalization of public services.

The adoption of online platforms by tax administrations for tax declaration and payment was a turning point. The Internal Revenue Service in the United States serves as an example. Another example is India’s introduction of the Goods and Services Tax in 2017. In France, the implementation of tax-at-source in 2019 also stands as a symbolic representation of the use of data in the French tax system.
These developments signify how data has become crucial to modernize and streamline tax systems globally.

Data is foundational to trust and stability

Proper data management strengthens the trust of customers, partners, and investors, just as a stable currency bolsters confidence in the economy. Data became a key element of trust and stability with the advent of the digital revolution, especially with the development of blockchain technologies in the 2010s.

Bitcoin, created in 2009, is arguably the most prominent example as a decentralized currency where trust is established not by a central financial institution, but by network consensus. The value and stability of Bitcoin rest on the transparency and immutability of transaction data recorded in the blockchain. Thus, data, when processed and stored in a transparent and secure manner, can serve as the foundation for trust and stability in a decentralized system. More broadly, data holds the potential to create trust in various fields, from smart contracts to online voting systems and many other applications.

Data is a facilitator of international trade

Much like currency facilitates international trade, data plays a growing role in global commerce, with the transfer of data between countries becoming a key element of trade agreements. Integrated supply chain management systems, e-commerce platforms, and online payment solutions are among the major innovations that have helped facilitate international trade.

The rise of the dominant e-commerce global marketplaces is another prime example of how data has propelled international trade. Spanning multiple continents, they leverage user data to recommend products, predict demand, set pricing strategies, and optimize logistics. Sellers, from different corners of the globe, utilize their data-driven insights to forecast product demand, manage inventory, and target customers. Through its comprehensive logistics and fulfillment services, these companies use data analytics to streamline international shipping, customs, and storage processes, making it easier than ever for sellers to reach global audiences.

It underscores the indispensable role of data in simplifying cross-border transactions, predicting global market trends, and democratizing access to international markets for businesses of all scales.

Data is a vector for regulating liquidity

As monetary policy regulates the amount of currency, regulations on data determine how they can be stored, shared, and utilized. The rapid expansion of digital financial markets has enabled the use of real-time data to analyze and predict market movements, as well as to automatically regulate liquidity.

Investment banks and hedge funds were among the first to adopt high-frequency trading, using algorithms to execute orders at a speed and frequency that are beyond a human trader. The May 6, 2010, flash crash, often referred to as the “Flash Crash,” is a notable example of the consequences of intensive data use in regulating liquidity.
While this event highlighted the risks associated with an excessive reliance on algorithms and data for liquidity regulation, it also underscored the critical importance of data in the modern functioning of financial markets.

Overall, data has emerged as a pivotal factor driving global economic structures, paralleling the influence once held exclusively by traditional currency.

It underscores its central role in a multitude of sectors, from economic policymaking to international trade. Drawing on its historical trajectory and expansive influence, it becomes evident that our current understanding of data’s value is only scratching the surface.

As we acknowledge the transformative power of data, it’s crucial to offer recommendations to harness its potential responsibly, ensuring a sustainable and equitable global data economy.

Let’s delve into strategic insights to bank on this newfound currency of the digital age:

Building central data backbones for a modern data economy

Central banks, such as the European Central Bank or the US Federal Reserve, play a major role in regulating and stabilizing currency. There is no equivalent entity to regulate data on such a scale. Today, just as there are Central Banks for currency, Central Data Banks are necessary.

Currently, vast amounts of data are held by a few tech giants. A central data bank could help decentralize the ownership of these data, thus reducing the power and control concentrated in a few hands. A central data bank could ensure equitable access to information, preventing certain businesses or entities from monopolizing data for profit.

The central data bank would be responsible for overseeing institutions that hold, process, and exchange data, just as central banks supervise financial institutions. It would establish standards for data protection, their ethical use, and would ensure compliance with these standards through audits and inspections.

Determining the rate at which data should be universally accessible

Inspired by the interest rate benchmarks used by central banks in the financial world, the benchmark access rate to data (BARD) would serve as a regulatory mechanism to control access to data stored in a central data bank. This rate would represent a measure of the ease (or difficulty) with which external entities can access this data. The lower the BARD, the more affordable it would be for entities to view or use the data stored in the central bank. Conversely, a high BARD would mean that access to the data is more restricted and costly.

It would be a strategic tool for promoting Research and Innovation: when the bank wishes to stimulate research, innovation, or competitiveness, it could lower the BARD. This would allow researchers, startups, and companies to take advantage of the available data, thereby fostering technological and economic development.
The establishment of the BARD would be the responsibility of a regulatory authority, likely a governmental entity or an independent body mandated for this function.

Balancing concerns about data privacy with contingency planning for data security

Drawing inspiration from the mandatory reserves imposed on banks by monetary authorities, Mandatory Data Reserves (MDR) would refer to a minimum portion of data that businesses and institutions would be required to store within a central data bank. This mechanism would aim to ensure the security, transparency, and regulation of data flow.

Just as banks are required to hold a fraction of their deposits in reserve, entities that collect, process, and store data would be obliged to deposit a certain proportion of these data in the central data bank.

The amount of data to be kept could be defined in terms of a percentage of the entity’s total storage capacity or the total volume of data processed.
These deposited data would remain the property of the originating entity but would be stored securely and centrally for various reasons, including regulation, oversight, and security. Storing data in a central reserve would promote greater transparency and enhanced accountability for entities.

Navigating the fine line between data accessibility and data exploitation

Similar to the open market operations used by central banks to regulate the money supply, Open Data Market Operations (ODMO) would refer to the transactions initiated by the central data bank on an open data market. The goal would be to regulate the quantity, quality, and availability of data in the digital economy.

ODMO would allow the central data bank to actively intervene in a data market, where datasets are exchanged. This intervention could take the form of purchases to inject data into the market or sales to withdraw data from the market or generate revenue. The price of these datasets would be determined by demand and supply in the market, just like securities in financial markets.

By purchasing high-value or rare datasets, the central data bank could make them available to researchers, innovators, and decision-makers, thereby promoting innovation and informed decision-making.

Ensuring individuals are fairly valued and compensated for their data

Every citizen could have a personal data account with the central data bank where they can voluntarily deposit some of their data. These accounts would be protected and secure, offering citizens complete control over who can access their data and under what conditions. Access to certain data could be subject to a remuneration system for the data owners. Companies, researchers, or other entities wishing to access specific data might pay fees. A portion of these fees could be redistributed to the citizens whose data are used. This remuneration would be proportional to the use and value of the data in question.

The central data bank could establish a mechanism to assess the value of different types of data based on their rarity, utility, etc. Citizens could then have an idea of the monetary value of their data, encouraging them to knowingly share more valuable or rare information. At the end of each period (month, quarter, year), the central data bank could redistribute a portion of its profits to citizens in the form of a “data dividend”. This dividend would be a recognition of the collective value of the data provided by the citizens and would be distributed based on each individual’s contribution.

Lending data responsibly

The concept of “Data Lending Facilities”, inspired by the lending facilities that central banks provide to financial institutions, would enable the provision of data for specific uses over a defined period, grounded in the idea that data can be treated as an asset, akin to money.

In the modern data-driven economy, not all institutions necessarily have the resources to collect, process, and store vast data sets. However, they might need this data for specific projects, studies, or innovations. Rather than forcing them to purchase or access these data on a permanent basis, a lending facility would allow them to borrow this data for a limited duration.

This access would often be limited to a specific platform to ensure security and monitor usage. This could be useful for institutions that need specific data for a temporary research project but don’t necessarily require permanent access to such data.

Standardizing the relative value of different data sets

Just as currencies have relative values to each other in the foreign exchange market, data could also be valued and traded based on certain criteria. This would introduce a form of standardization and regulation in data trading. Several factors could determine the value of data, such as its relevance, timeliness, rarity, specificity, quality, etc.

Specialized institutions or departments within the central data bank might be responsible for the regular evaluation of data sets. A centralized platform could be established where entities can offer their data sets for exchange, similar to a stock exchange.

Just like with currencies, the value of data would fluctuate based on supply and demand. Rare but highly demanded data sets could have a high exchange rate.
Such a system could introduce a form of standardization in how data is valued and traded.

Covering the intangible risks of Data breaches

In many countries, citizens’ bank deposits are insured up to a certain amount. There is no equivalent to “insure” personal data in the event of a breach or loss. The model of data deposit insurance has also become crucial. Data Deposit Guarantee Funds (DDGF) could be considered. Just as banks contribute to a deposit guarantee fund to protect customers’ money in case of a bank failure, companies that store and process data could be required to contribute to a similar fund for data. In case of data breach or loss, this fund could be used to compensate the affected individuals, whether through financial compensation or services.

Moreover, similar to bank deposit insurance that covers up to a certain amount per depositor, data deposit insurance could guarantee the security of the data up to a certain “quantity” or “value”. If someone loses data due to a breach, a predefined set of this data (for example, the most sensitive data) would be guaranteed or compensated.

Guaranteeing human rights take precedence over the surge in data collection

For many, the current rules and related sanction mechanisms for human’s data protection violations don’t seem to fully reflect the significance and sensitivity of personal data. In the financial sector, sanctions are designed to be as preventive as they are punitive. They are calculated to have a major financial impact on the offenders, while also deterring them from repeating their wrongdoings. Financial institutions can lose their ability to operate, which is a grave consequence.
A similar measure in the tech world could involve the suspension of certain activities, or even the shutdown of parts of a service.

Furthermore, citizens should be better informed about their data rights and how their data are used. Strengthening individuals’ rights to request the deletion of their data could limit companies’ abilities to indefinitely store information without valid reason. This should involve providing clear information to every data owner about all users of their data.

And just as with international financial standards, there could be a benefit to having global standards for data protection and sanctions, thus avoiding “data havens” where companies might try to relocate to escape regulation. Close collaboration between countries would be essential to ensure the effectiveness of sanctions and prevent companies from merely shifting their operations.

Curbing the negative impact of data speculation in the market

Speculation is a well-known concept in the financial world, where players buy and sell assets hoping to realize future profits. While “data speculation” isn’t a commonly used term, the idea captures the essence of a growing phenomenon where data is collected, stored, and traded with the aim of profiting from its future use.

Companies might collect data without an immediate or specific use in mind, hoping that it might be useful or profitable in the future. This is particularly true for tech companies that have the capabilities to store vast amounts of data. Furthermore, just as excessive speculation can create financial bubbles, a “data bubble” might emerge, where the perceived value of the data far surpasses its actual utility.

In the same way that certain financial mechanisms impose limits on speculation, caps could be implemented to restrict the amount of data a company can collect without justification. Just as financial transactions can be taxed to discourage speculation, a tax on the collection, storage, or trade of data could be considered. Companies might be required to disclose the nature, quantity, and usage of the data they collect, thus allowing regulators and the public to monitor speculation.

Ensuring transparent reporting without hindering data-driven industries

The reporting obligation for financial institutions regarding suspicious activities aims to combat money laundering, terrorist financing, and other illicit activities. In the world of data, the notion of “suspicious data” is different, but the underlying principle – accountability and transparency – remains. This might include unauthorized access to databases, accidental exposures or data theft, unusual data access patterns, unexpected requests for large amounts of data, or data transfers to unknown destinations that might be deemed suspicious.

Regulations concerning reporting obligations vary considerably from one country to another. This can create confusion for international companies and allow some to avoid reporting by exploiting these inconsistencies. Moreover, in some places, fines or penalties for non-reporting or late reporting are minimal, offering little incentive for compliance. Promoting international guidelines or treaties on data breach reporting could help establish a minimum compliance baseline.

The emergence of data as a form of currency redefines traditional paradigms of value and exchange. This transformation unfolds with unmatched opportunities and risks, intertwined with pressing ethical concerns. While financial regulatory mechanisms have been refined over centuries in response to crises and innovations, data, in its newfound monetary stature, is in its infancy.

Concepts such as transparency, fairness, security, and accountability, fundamental in the financial sector, can serve as cornerstones in designing regulatory frameworks for data. In essence, while acknowledging data’s uniqueness as a currency, the financial regulatory system provides an opportunity to learn from its effectiveness and its limits. 

By marrying these lessons with a nuanced understanding of data’s specifics, we can hope to establish a balance that maximizes the benefits of this new currency while minimizing its potential risks to individuals and society at large.

About the Author

Hamilton Mann is the Group VP of Digital Marketing and Digital Transformation at Thales. He is also the President of the Digital Transformation Club of INSEAD Alumni Association France (IAAF), a mentor at the MIT Priscilla King Gray (PKG) Center, and Senior Lecturer at INSEAD, HEC and EDHEC Business School.

The post Banking on Data: the World’s First-Ever Common Currency appeared first on The European Business Review.

In today’s data-driven world, predictive analytics has become the key benefit of making informed decisions and staying ahead of the curve. Did you know that businesses that harness the power of predictive analytics are 2.5 times more likely to exceed their sales goals and 3 times more likely to retain their customers? 

This blog will dive deep into the fascinating world of predictive analytics, unraveling its mysteries and showing you how data can be your crystal ball for forecasting future trends. So, buckle up as we embark on this journey into the future, where data holds the key to tomorrow’s successes!

What is Predictive Analytics?

Predictive Analytics, in simple terms, is like a digital fortune teller that uses data to make predictions about the future. Imagine it as a super-smart crystal ball for businesses, scientists, and even weather forecasters.

Here’s how it works: Predictive Analytics gathers a ton of information from the past – like sales records, customer behavior, or even past weather patterns. Then, it uses fancy math and computer wizardry to find hidden patterns and trends in all that data.

Once these patterns are spotted, ML in predictive analytics can make educated guesses about what might happen next. For businesses, predicting what products customers will buy or when machines need maintenance. For weather forecasters, it helps in telling us if we should pack an umbrella tomorrow.

Predictive analytics turns heaps of data into valuable insights, helping us make smarter decisions and prepare for what’s coming down the road. It’s like having a wise old oracle but in the form of numbers and algorithms!

The Role of Data

Data is the bedrock of predictive analytics. It’s like the bricks used to build a house. Without good data, predictive analytics will work well. Think of data as pieces of information – numbers, words, or pictures – that tell a story. This story is what helps us predict the future.

Types of Data Used in Predictive Analytics

• Structured Data: This data type is organized and neat, like a well-arranged bookshelf. It includes things like numbers in spreadsheets, dates, and categories. Predictive analytics loves structured data because it’s easy to work with.
• Unstructured Data: Unstructured data is a bit messy. It includes things like emails, social media posts, and even images. Predictive analytics uses special tools to make sense of this chaos and find hidden treasures of information.

Predictive analytics needs good data. Data quality means making sure the information is accurate and up-to-date. Cleaning data involves fixing errors and removing duplicates to ensure our predictions are trustworthy.

So, in predictive analytics, data saves the day by providing the clues needed to predict the future!

Techniques in Predictive Analytics

Uses of big data and analytics in predictive analytics utilize smart techniques to turn data into predictions. Think of these techniques as tools in a detective’s kit, helping us solve the mystery of the future.

1. Regression Analysis

Regression analysis is similar to drawing a line through data points on a graph. It helps us understand how one thing (like price) is connected to another thing (like sales). For example, it can tell us how an increase in advertising spending might affect product sales.

2. Time Series Analysis

Imagine looking at a video of a plant growing. Time series analysis is a bit like that but with data. It looks at how things change over time. Weather forecasts use time series analysis to predict future temperatures and rainfall.

3. Machine Learning Algorithms

Machine learning is like teaching a computer to learn from data, just like you learn from your experiences. Computers use different algorithms (fancy math) to make predictions.

• Decision Trees: These are flowcharts that help make decisions. They are used to determine the best choice at each step.
• Random Forest: Imagine a forest with many trees. Each tree has an opinion. Random forests collect all these opinions to make a better prediction.
• Neural Networks: These are inspired by how our brains work. They can find complex patterns in data, like recognizing faces in photos.

4. Deep Learning and Neural Networks

Deep learning can find really, really tricky patterns in data. It’s often used in things like speech recognition and self-driving cars.

Predictive analytics uses these tools and techniques to peek into the future. It’s like having a crystal ball, but one that relies on math and data instead of magic!

Challenges in Predictive Analytics

Predictive analytics is powerful, but it faces its own set of challenges. These challenges are like obstacles on a path that the data detectives must navigate.

1. Data Privacy and Security

Data needs protection. Predictive analytics often use personal or sensitive data, like your shopping history or medical records. Keeping this data safe and respecting people’s privacy is a big challenge.

2. Bias and Fairness

Sometimes, data can be biased, like a scale that’s a few pounds off. If the data used to train predictive models is biased, the predictions can be unfair or inaccurate. Detecting and reducing bias is a crucial challenge in predictive analytics.

3. Interpretability of Models

Some predictive models can be tricky to understand. Making models more interpretable so that humans can grasp how they make predictions is a challenge.

4. Scalability

As more and more data pours in, predictive analytics systems need to handle it all. Think of it as handling a growing crowd at a concert. Another challenge is ensuring predictive analytics can scale up to handle massive amounts of data.

Despite these challenges, predictive analytics continues to evolve and help us see the future. It’s like solving a puzzle – tricky but worth it in the end!

Best Practices in Predictive Analytics

Following some best practices is important to make the most of predictive analytics. These are the best practices that help ensure accurate predictions and successful outcomes.

1. Data Governance

Think of data as a valuable treasure. Data governance involves setting rules for how data is collected, stored, and used. Good data governance ensures the data used in predictive analytics is reliable.

2. Feature Engineering

Feature engineering is about creating new data features or variables to improve prediction accuracy. This might involve combining existing data in clever ways or creating new measurements.

3. Model Interpretability

A magic trick is more impressive when you know how it’s done. Predictive models are similar. Making them interpretable means making sure we can understand why they make certain predictions. This builds trust and confidence in the results.

4. Continuous Monitoring and Updating

Predictive models need to be constantly checked and updated as new data becomes available. Continuous monitoring ensures that predictions stay accurate over time.

Following these best practices ensures that predictive analytics remain a valuable tool for making informed decisions and researching the future. It’s all about using data wisely and responsibly!

The Future of Predictive Analytics

Predictive analytics has already revolutionized how businesses make decisions, and its future promises even more exciting developments. Let’s explore what lies ahead in predictive analytics and how it’s poised to forecast future trends with data.

Enhanced Machine Learning Algorithms: One of the most exciting prospects for predictive analytics is the continuous improvement of machine learning development algorithms. These algorithms are the heart and soul of predictive analytics, allowing computers to learn from data and make predictions. 

In the future, we can expect these algorithms to become more powerful and efficient, enabling businesses to extract insights from data sources that were previously too complex to analyze effectively.

Real-time Predictions: Imagine having the ability to make predictions in real-time. This is where predictive analytics is heading. With the growth of the Internet of Things and the increasing availability of real-time data streams, predictive models will become more responsive and adaptable. 

Businesses can make decisions based on recent information, leading to quicker and more accurate responses to changing trends.

Improved Data Integration: Predictive analytics is most effective when harnessing various data sources. In the future, we can anticipate even better data integration techniques. 

This means businesses can combine data from different sources, like social media, financial records, and customer behavior, to understand their operations and customer preferences better.

Explainable AI: While an AI and machine learning development company has made great strides, there’s still room for improvement in transparency and explainability. 

Future predictive analytics tools will likely focus on making the reasoning behind predictions more understandable to humans. This will be especially important in industries with strict regulations or ethical concerns, such as healthcare and finance.

Personalized Experiences: Predictive analytics is already used extensively in creating personalized recommendations in e-commerce and content suggestions in streaming services. 

In the future, we can expect even more tailored experiences as predictive models become more accurate in understanding individual preferences. This will lead to more engaging and relevant interactions with customers.

Ethical Considerations: As predictive analytics becomes more powerful, ethical considerations will play an increasingly significant role. Businesses must consider privacy, bias, and fairness issues in their predictive models. Addressing these concerns will be crucial to building customer trust and avoiding legal and reputational risks.

Democratization of Predictive Analytics: In the future, predictive analytics tools may become more accessible to a broader range of users. This democratization of analytics could empower smaller businesses and individuals to harness the power of predictive modeling without advanced technical skills.

The future of predictive analytics is bright and promising. As technology evolves continuously, businesses and individuals alike can access more advanced tools for forecasting future trends with data. 

However, it’s essential to remember that with great power comes great responsibility. When you hire machine learning app developers, ethical considerations and transparency will be crucial as we navigate this exciting future of predictive analytics.

Conclusion

As we wrap up our journey through the fascinating world of predictive analytics, one thing is crystal clear: data is the key to unlocking tomorrow’s secrets today. 

From businesses staying ahead of the competition to meteorologists predicting storms, the power of data-driven predictions is undeniable. As we step into the future, armed with smarter algorithms and ethical guidelines, predictive analytics will continue to be our trusted crystal ball. 

So, let’s embrace this data-driven era, make informed decisions, and shape a brighter and more predictive future for all. Remember, the future is not set in stone, but we can certainly carve a path to success with predictive analytics.

The post Predictive Analytics: Forecasting Future Trends with Data appeared first on The European Business Review.

Data is one of the primary assets of almost any business today, but there’s rarely a value given to an organisation’s data.

When it comes time to migrate your data to a new system as part of your digital transformation efforts, you can’t just take what you have and move it over. The lift and shift approach isn’t efficient and may, in the long run, cost more and produce less.

Rather, you must prepare your data for digital transformation. This requires taking a close inventory of what you have, what you need to keep and what you can get rid of.

Only when you’ve done this is your data really going to be ready for digital transformation.

A massive proliferation of documents and data 

Today’s organisations have massive volumes of data, some of it structured, but much of it is unstructured. And it’s continuing to proliferate. Imagine if data were like objects in your house. Let’s say you’ve lived in this house for years, and every room is filled to overflowing. Maybe the house is so full that all you can manage is to get the front door open, climb the staircase and throw yourself on the bed. It’s just that full. Now, what if your landlord sells the house and suddenly you have to move to a one-bedroom apartment. You certainly can’t take decades’ worth of stuff with you; you’re going to have to do some serious cleaning and ensure you only take with you the things you need.

This is the current state of affairs for organisations with huge systems that are trying to digitally transform in 2023; they’re trying to move, but they’ve got all this data “stuff.” That is a real problem, because they’ve got no idea what’s in the “house” unless they go through it. But they haven’t got time to do this, and they’ve got no idea what to throw away or what’s valuable.

This is the major issue: what is valuable data? Is it the data that’s 20 years old? Is it the stuff that’s only a year old? Maybe for some people in the organisation, there is still value to that 20-year-old data. Or maybe it’s unnecessary and you need to clear it out.

Taking stock: What’s needed and what’s not

Professional organising consultant Marie Kondo has won acclaim for her simple recommendation on tidying up that asks people to think about whether something still sparks joy. If it doesn’t, she instructs, you should let go of it. That’s a good metaphor for getting rid of data. So much data is being held onto by organisations, not because they really need it but because that’s how they’ve always done it. And going through all that data – maybe a hundred terabytes or more – can seem like a daunting and complex undertaking.

When the time comes, though, you’ll have IT on one side wanting to get rid of as much data as possible. You’ll have the line of business on the other side not wanting to get rid of any of their data. This creates a data tug of war. Eventually, there will be an evolution of AI-based tools that can help sort this out, but the solutions aren’t quite there yet. 

Six steps for preparing the data

How do you get started with tackling this effort?

Setting the stage: First, you’ll need to appoint the team that will take responsibility for it and develop a plan that will make the most of it. To guarantee that the time efficiencies will be maintained throughout data gathering, incorporate automated processes with strong AI underpinnings and reliable rules from the beginning. Basically, control the archiving process to guarantee that you reach data storage nirvana while being compliant.

Assess your resources and systematically combine them: Data can be replicated, readily fragmented, and exists across multiple platforms due to the use of numerous applications. As a result, it is important to aggregate the data. 

Evaluate the data in your stockpile: If there is proof that the data has value, keep it. And conduct your due diligence to ensure that what is left complies with legal and regulatory requirements. If you don’t need to keep it for those reasons, throw it away.

Check for accuracy: This is a key part of determining value. Inaccurate data can cause problems in an organization’s business processes.

Watch out for “dark data”: Dark data can exist in both structured and unstructured data; it resembles an iceberg whose top you can see but whose body you can’t because it’s below the surface. The amount of data that needs to be maintained for regulatory purposes means that this type of data can increase rapidly year after year, which presents a challenge for the CIO. More money and risk are involved in storing this kind of data than it is worth, based on the value derived from it. To get around this, initial automation and archiving efficiencies will only permit storage of the necessary data.

Conduct data analysis: To fully benefit from the potential presented by digital transformation, the organisation must not only acquire and analyse the data but also preserve it and make sure it is properly cleaned and stored.

Take the time to create value

Data is one of the primary assets of almost any business today, but there’s rarely a value given to an organisation’s data. But because of the inherent and potential value of your data, you must take time to go through it and evaluate it to truly succeed with your digital transformation. Use the steps outlined above to ensure that your data is properly prepped for migration.

This article was originally published on 28 May 2023.

About the Author

Nick Parkin is the CEO and Founder of Proceed Group, the expert in SAP data management, with over twenty years of experience in SAP archiving, content management and legacy systems decommissioning. Parkin established Proceed Group in 2001 to provide expert data archiving solutions for organizations struggling with increasing data volumes.

The post Preparing Data for Digital Transformation appeared first on The European Business Review.

Trends in Data Science and Business Analytics

• Advancements in Machine Learning and Artificial Intelligence

Knowledge from Machine Learning and AI courses are revolutionizing data analysis, automating processes, and enabling predictive analytics, making data scientists’ roles more data-driven and innovative.

• Big Data and Data Engineering

With the explosive growth of data, data engineers play a pivotal role in managing and structuring vast datasets, ensuring efficient data processing and storage.

• Integration of Data Science with Cloud Computing

Cloud computing facilitates scalable data processing, storage, and accessibility, leading to increased demand for data scientists with expertise in cloud technologies.

• Role of Data Visualization and Interpretation

Data visualization is becoming an essential skill for data professionals, enabling them to present complex insights visually compellingly to drive better decision-making.

Emerging Industries and Applications

• Data Science in Healthcare and Medicine

Data-driven healthcare solutions, personalized medicine, and predictive analytics are transforming the healthcare industry, creating exciting opportunities for data scientists.

• Business Analytics in E-Commerce and Retail

E-commerce platforms leverage data analytics to enhance customer experience, optimize supply chain management, and drive sales through targeted marketing strategies.

• Data-Driven Decision-Making in Finance and Banking

Financial institutions rely on data science to detect fraud, assess credit risk, and optimize investment strategies, making data scientists invaluable assets in the finance sector.

• Applications of Data Science in Renewable Energy and Sustainability

Data-driven approaches are vital for optimizing renewable energy production, reducing carbon footprints, and achieving sustainable development goals.

Skills and Qualifications for Future Data Scientists and Business Analysts

• Technical Skills: Programming, Statistics, and Machine Learning

Proficiency in programming languages (Python, R), statistical analysis, and machine learning algorithms are fundamental for data scientists and business analysts.

• Domain Knowledge and Specialization

A deep understanding of specific industries (e.g., finance, healthcare) allows data professionals to provide domain-specific insights and solutions.

• Soft Skills: Communication, Problem-Solving, and Critical Thinking

Effective communication, problem-solving, and critical thinking abilities are essential for data professionals to collaborate with teams and derive meaningful insights from data.

What are the challenges in Data Security?

Data security faces several challenges in today’s digital landscape. Some of the key challenges include:

• Cyberattacks: Cybercriminals constantly evolve tactics to breach security measures and gain access to sensitive data. Common cyberattacks include malware, ransomware, phishing, and distributed denial of service (DDoS) attacks.
• Data Breaches: Data breaches can occur for various reasons, such as human error, insider threats, or sophisticated hacking techniques. Breaches can lead to the exposure of personal information, financial data, or intellectual property, causing severe reputational and financial damage to organizations.
• Insider Threats: Malicious or negligent actions by employees or authorized users can pose significant security risks. Insiders’ sensitive data access may intentionally or unintentionally compromise data security.
• Cloud Security: With the growing adoption of cloud computing, ensuring data security stored in cloud environments has become a significant concern. Organizations must address cloud-specific security challenges such as data encryption, access control, and compliance.
• Lack of Awareness and Training: Cyber awareness and proper employee training can lead to security vulnerabilities. Employees may fall victim to social engineering attacks or unknowingly expose sensitive information.
• Data Privacy and Compliance: Data security must align with various data protection regulations such as GDPR, HIPAA, and CCPA. Compliance with these regulations requires organizations to implement robust security measures and ensure user data privacy.
• Third-Party Risks: Collaborating with third-party vendors or service providers can introduce additional security risks. Organizations must assess and manage the security practices of their partners to safeguard shared data.
• Mobile and IoT Devices: The proliferation of mobile devices and Internet of Things devices creates new entry points for cyberattacks. Securing these devices and the data they collect is a significant challenge.
• Advanced Persistent Threats (APTs): APTs are sophisticated and stealthy cyberattacks that target specific organizations for long periods. Detecting and mitigating APTs requires advanced security tools and expertise.
• Rapid Technology Advancements: As technology evolves, new security challenges emerge. Adopting emerging technologies such as AI, blockchain, and quantum computing requires careful consideration of security implications.

Addressing these challenges requires a multi-layered data security approach involving robust encryption, access controls, security training, threat detection, and continuous monitoring to safeguard valuable data from potential threats.

The Future Role of Data Scientists and Business Analysts

In the future, the role of data scientists and business analysts will go beyond analyzing data and generating insights. They will need to master the art of storytelling to effectively communicate complex findings to non-technical stakeholders in a compelling and actionable manner. Data professionals will be crucial in supporting decision-making processes and shaping business strategies with data-driven insights, becoming integral members of strategic planning teams. The growing demand for data expertise will create new opportunities for data scientists to offer consulting services and even establish startups, addressing data-related challenges and providing innovative solutions across various industries. Embracing these evolving roles will enable data professionals to thrive in the dynamic landscape of data science and business analytics.

Formal Education: Degrees and Certifications

Formal education, including degrees and certifications, is essential for aspiring data professionals looking to succeed in the dynamic field of data science and business analytics. Earning a degree in data science, computer science, or related fields provides a strong foundation in data analysis, machine learning, and data management. Specialized master’s programs in business analytics or data science focus on applying data insights to real-world business challenges. Certifications from reputable organizations and cloud service providers like AWS, Google Cloud, and Microsoft Azure validate expertise in specific tools and methodologies, enhancing career prospects and credibility in the job market.

Online Learning Platforms and Data Science Bootcamps

The rise of online learning platforms has revolutionized professional development in data science, providing accessible and flexible opportunities for upskilling. Platforms like Great Learning, Coursera, Udemy, edX, and DataCamp offer diverse data science and business analytics courses and bootcamps taught by experts. Aspiring data professionals can also explore comprehensive programs like the Data Science Course offered by Scaler, which includes hands-on projects and mentorship to prepare learners for real-world challenges. Data science bootcamps provide immersive training with real-world projects, making them ideal for career switchers seeking expedited entry into the field. These programs also facilitate networking with potential employers and data science professionals.

Continuous Learning to Keep Up with Industry Trends

Data science and business analytics are dynamic and constantly evolving, with new tools and technologies emerging regularly. To remain competitive, data professionals must commit to lifelong learning and actively keep up with industry trends by attending conferences and webinars and joining data science communities. Engaging in online forums and open-source projects fosters a collaborative learning environment, showcasing dedication to professional growth and making them more appealing to potential employers in the fast-paced world of data science.

Interviews with Data Science and Analytics Professionals

Interviewing experienced data science and analytics professionals provides invaluable insights into their career journeys, challenges, and triumphs. Aspiring data scientists can gain practical advice, learn about diverse paths to success, and understand the essence of continuous learning and staying updated with the latest advancements in data science. Hearing about the educational background, certifications, and specific skill sets instrumental in professionals’ career growth can guide aspiring data scientists in building a solid foundation, gaining relevant experience, and adopting a proactive problem-solving approach. Understanding how experienced practitioners have tackled data-related challenges enhances newcomers’ preparedness and navigation in data science.

Case Studies of Successful Data Projects in Various Industries

Analyzing real-world case studies of successful data projects across diverse industries illustrates the significant impact of data science and business analytics on organizational growth and success. These case studies showcase how data-driven insights have been leveraged to solve complex problems, optimize processes, and make strategic plans that drive business growth. Aspiring data professionals can better understand how data science is applied in different sectors, such as transforming marketing campaigns to target the right audience or improving supply chain management through predictive analytics.

Industry insights and perspectives through interviews and case studies present a holistic view of the data science and analytics domain. Aspiring data professionals can draw inspiration, learn from experienced practitioners, and understand the immense potential of data-driven decision-making across industries. By staying informed about successful data projects and learning from seasoned professionals, individuals can make informed career choices and prepare for the exciting and evolving field of data science and business analytics.

Future Outlook and Predictions

The future outlook for data science and business analytics careers is highly promising, with an ever-increasing demand for skilled professionals. As the volume of data generated grows significantly, businesses recognize the importance of data-driven decision-making, leading to job opportunities for data professionals. According to various industry reports and projections, the growth rate of data science and business analytics jobs is expected to be significant, outpacing many other professions.

Organizations across multiple sectors, including finance, healthcare, retail, and technology, heavily invest in data analytics to gain valuable insights from their data. This creates many roles for data scientists, data analysts, business analysts, data engineers, and other related positions.

Impact of Technological Advancements on Careers

Technological advancements continuously shape the data science and business analytics landscape. To thrive in this dynamic field, data professionals must adapt and embrace new technologies that enhance data analysis and interpretation. Artificial Intelligence and machine learning algorithms transform data analysis by automating tasks, making predictions, and identifying patterns from massive datasets. Data professionals must possess AI skills to leverage their potential effectively.

Cloud computing has become integral to data storage, processing, and analytics. Cloud-based solutions provide scalability, flexibility, and cost-effectiveness, making them indispensable for organizations of all sizes. Data professionals must be well-versed in cloud technologies and understand how to leverage cloud platforms for seamless data analysis.

Opportunities for Data Professionals in Emerging Technologies

Emerging technologies offer exciting opportunities for data professionals to explore new realms and contribute to cutting-edge projects. The Internet of Things helps gather data from connected devices and sensors and enables data scientists to analyze and interpret real-time data from various sources. As IoT applications expand, data professionals can contribute to optimizing processes, predicting outcomes, and improving user experiences.

Blockchain technology is another emerging field with immense data security and integrity potential. Data professionals can explore blockchain applications in industries like finance, supply chain, and healthcare to ensure the immutability and transparency of data.

Quantum computing is on the horizon, promising to revolutionize data processing and optimization. Data professionals who embrace quantum computing concepts can explore its applications in solving complex problems and performing data-intensive calculations at unprecedented speeds.

Conclusion

Data science and business analytics are rapidly evolving fields with immense potential for career growth and impact on organizations. Aspiring data professionals should focus on acquiring the right skills, staying updated with industry trends, and developing strong domain expertise to excel in their careers.

The Ever-Changing Landscape of Data Science and the Exciting Path Ahead:

Data science and business analytics offer a dynamic and exciting career path, with opportunities to make a significant impact on businesses and society as a whole. Embracing continuous learning and staying adaptable will be crucial for success in this rapidly evolving field.

The post The Future of Data Science and Business Analytics: A Career Perspective appeared first on The European Business Review.

If you’ve heard of quantum computing but, amid the hype and hullabaloo of all the other “latest things” in the tech sphere, you’re a little hazy about what the term means and, equally importantly, whether you should be sitting up and taking more notice of it … simply read on!

KEY TAKEAWAYS

History appears to be repeating itself. Several years ago, when artificial intelligence (AI) was just entering our daily business conversations, there was much confusion over what this innovation would – and wouldn’t – accomplish for businesses.

Interest in quantum technology has recently expanded significantly, especially as worldwide competitiveness, particularly between American and Chinese enterprises, has increased. Quantum technologies, like AI back then, seem to be cloaked in mystery, since its three techniques – quantum computing, quantum simulation, and quantum sensing – have followed unique development and commercial deployment routes. While there exists a plethora of publicly available information on quantum technology developments, a brief primer on the nature of the technology, its varied techniques, and business implications is probably useful, if not valuable, to those who want to quickly jump into this subject.

Anything but computing

While it is impossible to explain quantum computing in detail here, an analogy can help with understanding conceptually how it works. Consider getting through a two-dimensional maze. A regular computer has to run one path after another until it gets to the exit. If, for instance, there are 256 possible paths to do that, the computer would have to try each of them in turn. In contrast, a quantum computer can run all 256 paths at once to find the way out. The key is that quantum computing is not only processing information exponentially faster, but also handling a wider set of variables simultaneously.

Quantum technology is believed to make use of physical phenomena that can’t be effectively demonstrated or simulated by simple classical physics. Calculating with qubits in a quantum environment, quantum computing is mainly suited to big or complex tasks such as optimisation, simulation, and analysis of big data with the use of artificial intelligence. Just as there is no real intelligence in the term “AI”, the term “quantum computing” has probably got us off to a confusing start. A common misconception is that quantum computers are essential in order to capture the advantages conferred by quantum physics, rendering “classical” computers obsolete. Yet, at the current stage of development, quantum hardware is very delicate and can only function correctly under very specific conditions. Hence, it will be years before quantum computers can reach commercial scale. This, in turn, means that, in the foreseeable future at least, new quantum computing devices will be working in conjunction with the plumbing system established by conventional machines.

Quantum for business

While the business community is increasingly interested in hearing about new applications of quantum technology in a commercial context, in general there are four areas on which quantum technology is expected to have an impact in the near future:

Encryption

Cybersecurity is probably the most widely discussed potential disruption that quantum would create, as it would threaten the safety of the digital and big data ecosystem of enterprises. The reason: the way quantum computing approaches mathematical problems makes it possible to crack the various forms of encryption that we rely on today. Indeed, as early as 1994, scientists showed that this is doable, provided that the quantum devices are sufficiently powerful. Peter Shor, specifically, demonstrated how simple it would be for a theoretical quantum computer to break many commonly used public-key encryption algorithms, such as RSA and Elliptic Curve Cryptography, currently in use at the individual, commercial, and national levels to safeguard the confidentiality and privacy of sensitive data and information. Even though quantum machines today have yet to bust the encryption techniques currently in use, the threat is getting more real with more powerful quantum computers coming online. Threats to industry-standard cryptographic algorithms will lead to a series of changes to the cybersecurity industry. Concerned cryptographers attempted to create new encryption algorithms, known as post-quantum cryptography, that are impervious to quantum systems and will fortify the protection of customer data, fulfilment of business processes and transactions, and communications. For instance, the US Department of Homeland Security and the Department of Commerce’s National Institute of Standards and Technology have partnered to offer a road map for enterprises to shield themselves from cybersecurity threats raised by quantum computing. Consequently, a new wave of post-quantum encryption algorithms such as lattice-based cryptography, code-based cryptography, and hash-based cryptography emerged. Another response was the development of quantum key distribution, or QKD, in which sender and receiver use quantum methods to establish symmetric keys.

Businesses should also react promptly. One response is to upgrade their compliance strategies and tools with quantum-proof policies and procedures, in order to be able to safeguard private digital processes, data, and transactions. If businesses react wrongly and poorly to cybersecurity risks raised by quantum computing, they risk losing their market advantage and potentially damaging their brand. By spotting flaws in third-party software and hardware and enclosing their proprietary information, quantum computing has the potential to damage intellectual property and the supply chain of enterprises. Given that quantum computing could quickly spot and take advantage of security flaws, nefarious practices may also proliferate. Advanced cyberattacks can be carried out by malicious hackers who take advantage of flaws in encryption algorithms and other security measures.

Simulation

For pharmaceutical companies, lengthy, complex, and expensive drug discovery is often the result of serendipity and luck. Quantum-driven chemical engineering simulation enables scientists to better understand molecular structure and properties, making it easier to select and synthesise the right drug molecules. In fact, simulations also allow them to do so without actually synthesising them. This, in turn, could lower R&D costs and reduce timescales of drug discovery. A quantum computer computes all potential outcomes at once, rather than analysing each one separately. Parallel processing by quantum computing in particular has facilitated the efficient simulation of complex systems.²

Optimisation

The fact that quantum can consider all the possible solutions to a problem to help find the best one makes it uniquely capable of dealing with optimisation problems – maximising revenue or minimising costs – especially when the problems involve a lot of constraints and variables. This has huge business implications, as a range of operations can benefit from optimisation, including supply chain.

The use of optimisation algorithms as a tool for selecting the best solutions or procedures may transform businesses such as the transportation sector or energy systems, which rely on optimisation to measure their performance, such as the optimum location for wind turbines. In financial situations that involve high uncertainties and volatility in the behaviour of assets, prices, profits, and losses, quantum machine learning optimisation algorithms could be used to achieve goals like portfolio optimisation and credit scoring ³.

Sensing

Quantum sensing technology isn’t as mature as quantum cryptography or quantum communications, but their development and utilisation are promising. Quantum sensors, which use quantum mechanics attributes such as quantum entanglement, have increased the precision and accuracy of sensor technologies and methods of measurement, connectivity, and interactions with the environment around us ⁴. Quantum sensing gathers information at the atomic and individual atom levels with elevated advancements in sensitivity and spatial resolution, enabling the creation of immensely reliable, effectual, and meticulous sensor devices. The higher sensitivity of quantum sensing technologies, coupled with their higher specificity, improved accuracy in information collection, non-invasive nature of imaging, and multi-model imaging features, have redefined the biomedical imaging industry. Quantum sensors can recognise comparatively tiny variations in physical or chemical characteristics and distinguish between strongly linked molecules or signals, growing their specificity and sensitivity. Quantum convolutional neural networks (QCNN) can be employed in other industries, such as transportation, defence, and sustainability, by boosting the behaviour of training data. Quantum sensing in the skies, below the seas, or on the road can be utilised for navigation in situations where GPS is not obtainable, because the quantum sensors are unjammable and suitable for use in all weather. This functionality has expanded the applications of quantum sensing in both military and non-military navigation ⁵.

Where are we now?

According to a recent survey, 48 per cent of British companies believe that quantum computing will play a commercial role by 2025⁶. Indeed, many of them see the need to establish proof points and strategies in the next two years, because they have a clear expectation that quantum computing will be transformative for their businesses within the next three to five years. The recent report by McKinsey shows that funding of start-ups working on quantum technologies has doubled to $1.4 billion in 2021 from 2020, and its market value has the potential to exceed $90 billion by 2040⁷. However, this could be too optimistic. If AI adoption is anything to go by, few companies will rush to take on a new technology, no matter how beneficial it can be. This is because fully embedding a new technology in company operations often poses significant challenges. They range from technical and IT infrastructure issues⁸ to non-technology-specific obstacles such as budget constraints and the risks if the technology fails⁹.

Nevertheless, despite this encouraging statistic, responses of formal institutions, such as new regulations on security and firm-based capabilities, as well as the lack of appropriate expertise and skill sets, may prevent the widespread deployment of quantum computing. Deployment of quantum computing in businesses requires an enterprise-wide transformation, including strategy, procedures and processes, culture, and people, as well as integration with other technology already in use. Top-level management and shareholder commitment, as well as the appropriate experience and knowledge among human resources, are required at the level of culture and people. Businesses should primarily adjust their current digital technologies to better align with the unique characteristics of quantum computing. Additionally, organisations should restructure their processes and procedures to better leverage technology. Existing technological solutions, whether software or hardware, should, on the other hand, be capable of working with quantum computing technologies. Overall, we expect a new wave of digital transformation, which we call “quantum transformation”, to occur across all dimensions of the business. Employing quantum computing necessitates visionary businesses that can envision how quantum computing will impact their businesses and what viable alternatives they can provide to remain competitive in the market.

Quantum computing as a service

Due to the numerous applications of quantum computing, new revenue models are being developed that provide quantum computing as a service (QCaaS), which would be cloud services that provide customers with access to quantum computing platforms via the internet. The cloud-based QCaaS service model employed by companies such as Honeywell, Amazon, IBM, and Google has the potential to accelerate the development of software stacks and the performance of various enterprise departments, such as customer analysis, marketing, R&D, and market analysis. Quantum-based data optimisation and analysis, as well as pattern and object recognition, will be applied to restructure business operations. Amazon Braket, for example, is an Amazon quantum computing service designed to “speed up scientific research and software development for quantum computing”¹⁰. Google’s Cirg, which is also an open-source quantum computing platform, is utilised for building and testing algorithms¹¹.

Impact on our society

Even though we may be years away from seeing quantum used at scale, this should not stop us from considering the societal impacts that the technology may generate. Obviously, optimisation of traffic, travel routes, and supply chains can open up opportunities to cut carbon emissions. A better ability to perform simulations brings faster drug discovery. By identifying new compounds and modelling complex interactions and processes in our body, we have a higher chance of finding new treatments for diseases, both old and new. Quantum-based simulations can also lead to the development of more efficient processes to produce nitrogen-based fertilisers; 40 per cent of the carbon footprint of a loaf of bread results from the nitrogen generated while making the fertiliser to grow the wheat¹². Furthermore, quantum computing can help us find a cheaper, cleaner, and less resource-demanding alternative to the lithium-ion batteries that power everything from iPhone to Tesla.

Impact on the environment

The technology could have an adverse effect on the environment, due to its electricity consumption. Quantum computer production, maintenance, and disposal might also harm the environment and produce waste. To lessen the unforeseen negative effects of quantum computing on climate change, firms must adopt energy-efficient computing and manufacturing techniques. However, there is a compelling argument in favour of quantum computing’s potential to revolutionise the economics of decarbonisation and the battle against climate change. According to recent studies, quantum computing will be able to simulate battery chemistry in ways that are not currently possible¹³. The creation of novel energy generation and storage technologies as well as the identification of better strategies for combating climate change will be made possible by quantum computing. Arguments are made that the potential of quantum computing to resolve simulation in the field of fluid dynamics will advance models, increasing our comprehension of foreseeable future hazards and enabling the use of appropriate mitigation and adaptation strategies. Moreover, energy infrastructure might be located more safely with the use of improved weather and climate models. The combination of quantum computing and quantum artificial intelligence may revolutionise renewable and sustainable energy, as well¹⁴.

Impact on drug discovery

Quantum computing can help the extremely lucrative but highly risky business of drug discovery. By identifying molecules more quickly and effectively, the technology can assist pharma and biotech companies win accelerating their computer-aided drug discovery (CADD) and replacing ineffective trial-and-error procedures with engineering-based methods. The pharma business will gain from quantum computing throughout the whole value chain, but its main effects will be experienced during the research stage. By lowering costs and raising the chances of success, the technology can facilitate protein engineering, design, and precision medicine, in addition to predicting protein structure and simplifying clinical trials¹⁵.

Impact on Defence

Quantum computing has a tremendous impact not only in civilian environments but also in a military context. The defence industry will benefit greatly from quantum position, navigation, and timing (PNT) devices that will facilitate navigation without the need for external references such as GPS. Quantum radar will also tremendously revolutionise underwater navigation in submarines and places where GPS signals are lost. In addition to carrying out standard object detection and identification operations, quantum radar systems are also capable of recognising and identifying RF camouflage platforms and weapon systems¹⁶. The appealing feature of a quantum radar is its capability of employing quantum states of photons to gather information about distant targets by improving measurement sensitivity.

So far, yet so close

We are only at the nascent stage of quantum computing. Hardware that can operate at scale economically is still rather remote. Despite this, it is clear that quantum technology offers bountiful opportunities to create a better world for all of us. For this reason alone, it is worth our while to familiarise ourselves with the technology and follow its developments and applications closely.

This article was originally published on March 29, 2023.

About the Authors

Mark Esposito is Professor at Hult International Business School and Harvard University’s Division of Continuing Education and works in public policy at the Mohammed Bin Rashid School of Government. He directs the Hult Futures Impact Lab. He co-founded Nexus FrontierTech and the Circular Economy Alliance. He has written over 150 articles and edited/authored 13 books. His next book, “The Great Remobilization” will be published by MIT University Press in the course of 2023.

Terence Tse is Professor of Finance at Hult International Business School. He is also a co-founder and Executive Director of Nexus FrontierTech, an AI scale-up. Terence has appeared on television, in radio shows, and in periodicals. He has given seminars, workshops, and speeches for and to some 50 organisations. Terence has written three books, with the next to be published by MIT Press in 2024. He is on the board of various entities, including Nexus FrontierTech, Thyreality, Tolar HashNET, and Circular Economy Alliance. Previously, he was in investment banking and consulting. Terence has a PhD from the University of Cambridge, UK.

Tahereh Sonia Saheb is a research fellow at Hult International Business School’s Future Readiness Lab. She is the author of over 22 papers on the adoption of digital technologies and their ethical implications. She has over seven years of experience as a digital consultant and strategist at various banks and organisations. She also established and founded the first DBA/MBA programme in digital banking.

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