The Transformative Role of Data Products in Financial Services

Financial institutions are leading the charge in AI investment, with spending expected to climb from $35 billion in 2023 to $97 billion by 2027—a 29% CAGR. Yet despite this momentum, many struggle to convert data into real business value. The issue is not a lack of technology or information but the prevailing mindset of treating data as a byproduct rather than a product.

Embracing data-as-a-product shifts the conversation from technical implementation to business impact. With clear ownership, structured contracts, and measurable outcomes, data products help institutions unlock insights, improve compliance, and deliver ROI in a competitive, AI-driven marketplace.

The financial services data crisis

The industry has poured billions into digital transformation, but persistent gaps remain. Compliance failures led to $4.6 billion in global fines in 2024, often tied to poor governance and data quality. Meanwhile, 70% of customers now expect personalized banking experiences that rival top tech firms. And as AI adoption accelerates, the stakes for reliable, well-governed data have never been higher.

Legacy data management—silos, project-based delivery, and unclear ownership—cannot keep pace. Valuable insights remain trapped, leaving business users unable to act. The cost of inaction compounds, exposing institutions to penalties, lost competitiveness, and missed opportunities.

To move forward, institutions must reframe data not as a technical burden but as a strategic product. This shift represents the bridge between ongoing investments and true business transformation.

What are data products in financial services?

Data products are ready-to-use, business-facing solutions that convert raw data into actionable intelligence. Unlike conventional data assets that demand technical skills, data products are designed for consumption—delivering insights to those who need them, when they need them.

For example, instead of a data warehouse requiring SQL queries, a Customer 360 Profile product gives relationship managers a unified view of client relationships, risk exposure, and cross-sell opportunities—no coding required. This approach reduces barriers, speeds decisions, and ensures that every data initiative ties back to business value.

Ultimately, data products bridge the gap between technical complexity and business utility. They ensure every investment translates into measurable outcomes and meaningful change.

The business case: ROI and impact metrics

Data products deliver value across revenue, efficiency, and risk—three pillars of financial services performance.

• Revenue growth: Personalized recommendation engines have been proven to increase cross-sell conversions, unlocking millions in new annual revenue. Customer segmentation has revitalized dormant segments, improving campaign ROI.

• Operational efficiency: Performance dashboards can reduce loan approval times and customer service wait times. Customer 360 implementations have shortened call handling times, improving both productivity and satisfaction.

• Risk management: Loan risk scoring reduces non-performing loans, while AML monitoring prevents illicit transfers and regulatory breaches. These improvements safeguard portfolios and avoid costly penalties.

The ability to show ROI is not just a business advantage; it’s become a necessity. For CDOs, data products provide a powerful way to justify ongoing investment while demonstrating tangible business impact.

By connecting technical execution directly to business outcomes, institutions can scale trust in their data initiatives and fuel long-term transformation.

Key attributes for financial services data products

Not all data products are created equal. To succeed, they must embody attributes that balance business value, usability, and compliance:

• Value-first approach: Tied directly to solving business problems with measurable ROI.

• Discoverability: Centralized marketplaces and searchable metadata.

• Compliance-ready contracts: Clear ownership and defined quality standards.

• Business-friendly design: Metadata and documentation accessible to non-technical users.

• Global accessibility: Stable identifiers supporting both GUI and API access.

• Trustworthiness: Transparent lineage, governance policies, and quality metrics.

• Reusable and composable: Modular, domain-driven design serving multiple use cases.

• Secure by design: Role-based access and compliance frameworks that protect sensitive data.

When consistently applied, these principles ensure data products not only work but deliver ongoing value across the enterprise.

Implementation roadmap for financial institutions

Transforming to a product-driven model requires careful sequencing. Institutions should:

1. Assess readiness: Evaluate governance maturity, technology infrastructure, and organizational culture.

2. Prioritize quick wins: Begin with low-complexity, high-impact use cases such as segmentation or dashboards that deliver results within 90–120 days.

3. Scale strategically: Move toward analytical products, AI-powered insights, and comprehensive customer profiles.

4. Invest in foundations: Modern cloud platforms, robust security, and strong API management enable growth.

Crucially, success also requires cultural change—executive sponsorship, training programs, and cross-functional collaboration that build data literacy across the enterprise.

By combining technical foundations with organizational readiness, institutions can accelerate adoption and scale with confidence.

Overcoming common challenges

Institutions should anticipate and plan for hurdles:

• Legacy integration: Middleware and staged rollouts reduce disruption.

• Compliance pressure: Automated lineage and monitoring help satisfy regulators.

• Data quality: Stewardship, validation, and continuous monitoring are essential.

• Collaboration silos: Shared metrics and cross-functional governance encourage alignment.

These challenges are real but surmountable. With a clear roadmap and product-driven mindset, financial institutions can overcome inertia and build scalable, compliant solutions.

Examples of financial services data products

Practical applications highlight the transformative potential of data products. Some of these are already in use, while others represent theoretical yet highly valuable opportunities for financial institutions seeking to turn data into measurable business outcomes.

Risk management data products

• Customer retention prediction scores can identify customers at risk of churn, enabling targeted interventions. Applied more broadly, this type of product could help banks preserve revenue, extend customer lifetime value, and optimize retention marketing investments.

• Loan default risk scores provide proactive insights into credit quality. By integrating borrower history, debt ratios, and macroeconomic indicators, banks can reduce non-performing loans and reallocate capital more effectively.

• Market risk exposure reports can consolidate trading and portfolio data into automated dashboards, helping treasury teams hedge against currency, interest rate, or equity risks. 

Operational excellence data products

• Fraudulent transaction alert APIs score transactions in real time, blocking or flagging suspicious behavior while reducing false positives.

• AML transaction monitoring feeds enrich transaction data with sanctions and adverse media checks, helping compliance teams detect hidden networks of illicit activity.

• Operational performance dashboard feeds can aggregate process metrics across functions such as loan approvals and call centers. These products enable staffing optimization and efficiency gains.

Performance analytics data products

• Investment portfolio performance and attribution datasets provide transparency into sector, geography, and security-level performance.

• Customer segmentation datasets group customers by shared behaviors and needs, enabling personalized marketing campaigns. 

Customer experience data products

• Customer 360 profiles unify account, interaction, and product data into a single, accessible view. Relationship managers and service agents benefit from faster call resolution and more effective cross-sell conversations. 

• Personalized recommendation engines can suggest next-best products based on transaction history and digital engagement. 

These examples, some already demonstrated, others theoretical but highly feasible, showcase how data products can transform financial services. By reducing churn, improving risk management, enabling personalization, and optimizing operations, data products provide a scalable foundation for future AI-driven use cases.

How the BBC built a customer 360 data product

The BBC embarked on a data product transformation to shift from fragmented analytics toward strategic, decision-aligned insights. Central to that initiative was its creation of a unified Search Metrics data product. 

Previously, analysts wrestled with siloed tables and inconsistent methodologies across platforms—mobile, web, and TV—making it difficult to derive cohesive insights. The BBC’s new data product unified these disparate sources into a standardized, shareable dataset that became a trusted starting point for investigations across the organization. As Nathalie Berdat, Head of Data and AI Governance, noted: “Alation is now really part of the workflow… Not just, ‘Let me double check,’ but ‘Let me start there.’”

This transformation fostered a cultural shift: success was defined not by maintaining countless individual tables, but by reducing complexity and delivering a single, useful product—one that a product manager or engineer could rely on without ambiguity. In the first week alone, over 200 users adopted the product; within a few months, that number grew to more than 500.(

Key takeaways from BBC’s customer-insight product rollout:

• They prioritized insight over volume, consolidating messy raw data into a clean, governed single source of truth.

• They demonstrated impact quickly, achieving rapid adoption through a product that aligned with user needs.

• They set the foundation for scale, embedding governance and tooling so that future data products could follow the same trusted, repeatable model.

The BBC’s experience shows how a single, well-designed data product can transform organizational culture, accelerate adoption, and create lasting trust in data. By focusing on usability and governance, they laid the groundwork for scalable, AI-ready insights that drive measurable business value.

The future of data products in financial services

AI/ML integration

The next wave of financial services innovation hinges on AI. Generative AI, fraud detection, and risk modeling require low-latency, well-governed data products enriched with metadata and lineage. Without these foundations, AI models risk bias, inaccuracy, and compliance issues.

Regulatory evolution

Regulators are raising the bar on explainability, privacy, and fairness in AI. Data products must adapt with built-in governance, bias detection, and compliance-ready design. At the same time, open banking and secure data sharing create new opportunities for monetization.

In short, data products are no longer optional—they are the vehicle through which financial institutions will manage risk, scale AI, and meet regulatory and customer demands.

The evolving CDO role

The Chief Data Officer’s mandate is shifting. Today, success is measured less by infrastructure management and more by the ability to link data investments directly to measurable business results. Data products give CDOs the framework to prove value, align with strategy, and drive enterprise-wide adoption.

→ Learn more about how the CDO role is evolving in this podcast interview, The CDO’s AI Opportunity.

Getting started with Alation

Implementing data products requires more than technology—it demands expertise and a structured approach. Alation provides both, helping financial institutions assess readiness, prioritize opportunities, and avoid common pitfalls.

Our proven frameworks guide organizations from initial assessment to measurable success, tracking KPIs such as time-to-insight, reduction in duplicate data requests, and improved collaboration. Regular reviews ensure that data products continue to deliver sustained business impact.

Now is the time to act. Institutions that embrace data products today will be better equipped to harness AI, meet regulatory demands, and deliver customer experiences that define the future of financial services. Book a demo with us today to learn more.