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How financial institutions can generate meaningful value.
Financial Services firms are sitting atop vast mountains of data and yet, paradoxically, many are more data-poor than ever.
Despite years of investment in data lakes, warehouses, and AI tooling, most institutions are struggling to deliver tangible business outcomes from their data assets. Instead of clarity and innovation, they face chaotic "data swamps": sprawling, ungoverned repositories filled with redundant, incomplete, and non-compliant information.
At a time when AI compliance, digital acceleration, and regulatory scrutiny dominate boardroom agendas, merely having data is no longer an advantage — having usable, trusted, and strategically aligned data products is. The institutions that understand this shift will thrive; those that don’t will find themselves overrun by more agile, data-native competitors.
This article explores how Financial Services organisations can transform their data landscapes and why starting the journey now is crucial if you’re wanting to evolve.
Legacy Data Lakes and Poor Governance Models:
The data lake was supposed to be the silver bullet. Pioneered over a decade ago, these vast pools of raw data promised flexibility and cost-efficiency. In practice, they often became dumping grounds, with minimal metadata, unclear ownership, and unchecked data quality issues.
In Financial Services, the problem is even more acute. Decades of mergers and acquisitions, combined with strict regulatory obligations, have created fragmented ecosystems riddled with technical debt. Without rigorous governance and active stewardship, the dream of a centralised, unified data repository has turned into an absolute nightmare.
The uncomfortable truth? Data lakes have largely failed FSIs, not because the concept was flawed, but because implementation prioritised speed over structure, quantity over quality.
The stakes are rising. Regulators such as the FCA, PRA, and emerging global standards like the EU AI Act now demand that Financial Services institutions not only manage data securely but support the explainability, traceability, and fairness of the AI models built upon it.
In this environment, ungoverned data is more than a missed opportunity; it's a compliance and a reputational risk. Institutions can no longer hide behind the complexity of their systems. Data must be fit-for-purpose, ethical, and defensible and AI systems must be explainable from input to output.
A Data Product is not just a dataset with a fancy label. It’s a well-defined, business-ready asset, combining data, governance, discoverability, and trust.
At minimum, every Data Product must be:
Discoverable - easily located through a searchable catalogue
Addressable - Accessed through a stable, standardised interface (e.g. APIs)
Trustworthy - Governed, validated and compliant by design
Self-describing - Documented with sufficient metadata
Secure - Controlled via role-based access management
Value-driven - Created with a clear business use case in mind
This also means shifting from a ‘project’ mindset to a ‘product’ mindset. Projects solve short-term needs. Products solve long-term capabilities. A project might clean up a single dataset, but a data product builds a reusable, scalable asset that serves multiple needs across the business.
The distinction is critical. Data products require ongoing ownership, continuous improvement, and measurable value over time - all of which demand a new operational model.
Importantly, monetisation doesn’t always mean selling data externally. Many institutions are exploring internal chargebacks or “data currency” models - where data products are valued, consumed, and even traded internally. These models reward producers of high-quality data, establish internal markets for consumption, and start to embed commercial discipline into how data is created and maintained.
For Financial Services organisations, the move to Data Products transforms data from an operational burden into a strategic enabler:
High-quality, explainable data becomes the norm, not the exception
When data is structured, traceable, and aligned to business domains, demonstrating compliance becomes frictionless
Well-curated Data Products can be monetised internally across divisions, and externally via data marketplaces
Data Product thinking aligns naturally with modular, cloud-native, and composable architecture strategies
Designing for Data Products, not just Data storage:
The era of "one giant centralised lake" is now over. Progressive Financial Services organisations are adopting Data Mesh principles treating data ownership as a domain-specific responsibility, decentralising stewardship while maintaining strong governance.
Key shifts include:
Domain-driven design - Data Products are owned by business-aligned teams (e.g., risk, lending, customer analytics)
Federated Governance - Governance is embedded locally, but standards are agreed centrally
Interoperability-first Architecture - APIs, common taxonomies, and metadata standards ensure smooth integration across products
The Importance of Data Marketplaces
A Data Marketplace isn’t simply a shop window; it’s an internal operating model.
In a mature Financial Services data marketplace:
Business users can discover and self-serve approved data products
Pricing models (internal chargebacks or actual monetisation) incentivise high-quality data creation
Compliance and security are embedded, not retrofitted
Some institutions are beginning to treat their internal marketplaces as “value exchanges” where data producers are credited (or funded) based on the reuse and impact of their products. These internal currencies allow organisations to track data ROI, direct investment to high-impact areas, and foster a culture where data is treated as a valuable, measurable asset.
External marketplaces are also rising. Institutions slow to curate their own data products risk losing competitive advantage to fintechs and hyperscalers who are already treating data as a productised service.
Breaking Down Organisational Silos
Embedding Governance Early
Scaling AI Enablement
Moving to a data product mindset forces a deep cultural shift. Data teams must work hand-in-hand with risk, compliance, and commercial functions.
Organisations clinging to a "technology-only" approach where the business is treated as a mere customer, not a partner will fail.
Success requires cross-functional collaboration, with clear roles, shared incentives, and a product-oriented approach to ownership.
Governance cannot be an afterthought. Compliance by design is the new standard.
Each Data Product must have:
Clear data lineage documentation
Embedded policies for access control, retention, and usage
Automated checks for data quality, bias, and regulatory compliance
If your AI solution can’t explain its inputs, you will face operational and regulatory consequences not in 2030, but today.
AI strategies will fail without clean, structured, well-governed data.
Data Products empower safe, scalable AI by ensuring every input is explainable, defensible, and legally compliant; critical in an environment where AI risk management is no longer optional.
By embracing data products, financial services organisations can achieve:
AI-First Readiness: Faster, safer, and more scalable AI deployments
Operational Resilience: Stronger risk management and compliance posture
Innovation Acceleration: Easier experimentation and faster time-to-value for new products and services
Regulatory Confidence: Simplified reporting and audit readiness
Revenue Generation: Internal and external monetisation of high-quality data products
As product thinking becomes the norm, so too does the opportunity to measure and maximise data’s contribution to organisational performance; not just as infrastructure, but as an engine for transformation.
The delta between institutions that master this, and those that don’t, will widen dramatically over the next 24 months.
At Affinity Reply, we understand that technology alone isn’t enough. We bring:
Specialist Expertise: Deep domain knowledge in Financial Services, enterprise architecture, and AI compliance
Business Alignment: Every data strategy is linked to tangible outcomes: improved ROI, enhanced resilience, competitive advantage
Flexible, Agile Delivery: Our models adapt to your organisation’s structure, maturity, and ambition
We help clients transition from project-based approaches to lasting product-based capabilities; designing the operating models, governance structures, and incentives that make data products sustainable. Our mission is to make your organisation self-sufficient, future-proof, and positioned for leadership in the data economy. If you’re ready to stop treading water in a data swamp and start building true, value-driven data products; Affinity Reply is ready to be your partner.