In Part 1, we covered the data doom loop – the cycle of complexity, inefficiency, and eroding trust in financial data that persists despite massive investments. The solution isn’t another sweeping architectural overhaul. It’s about fixing how data is defined, accessed, and governed at the source.
That’s where data products and data contracts come in. These concepts aren’t just buzzwords – they’re pragmatic, enforceable ways to bring order to the chaos. Done right, they ensure data is reliable, accessible, and aligned with both business needs and regulatory requirements.
Why financial services need a new approach
Financial institutions aren’t struggling with a lack of data. They’re drowning in it. Trading desks, compliance teams, and risk management systems all generate and consume vast amounts of information – often with no clear ownership, common definitions, or reliable way to verify accuracy. The result?
- Endless debates over which data source is “right”
- Duplicate efforts to clean, reconcile, and validate data
- Slow, painful regulatory reporting processes
- Data teams constantly firefighting instead of building
This isn’t just inefficient – it’s actively harmful. Poor data quality leads to missed opportunities, compliance failures, and an inability to respond quickly to market shifts.
Data products: The foundation of a scalable strategy
The first step out of the doom loop is treating data like an actual product. A data product is a well-defined, high-quality dataset designed for a specific business purpose, with clear ownership, governance, and built-in guarantees.
Key attributes of a good data product
- Owned by a domain, not IT: Trading data should be owned by trading, risk data by risk, and so on. Business teams define requirements, while technology enables access and enforcement.
- Includes metadata and documentation: No more guessing what columns mean or how data is derived. A proper data product documents lineage, quality rules, and intended use cases.
- Enforces data quality: Validation rules are built in, not bolted on. If data doesn’t meet standards, it doesn’t get published.
- Supports multiple access ports: Analysts want SQL, developers want APIs, and risk systems need batch exports. A good data product supports all of them without duplicating logic.
Data contracts: Enforcing quality and governance at scale
Data products are only as good as the guarantees behind them. That’s where data contracts come in. These are enforceable agreements between producers and consumers that define structure, semantics, and quality expectations. They ensure data remains trustworthy, even as systems evolve.
Strong data contracts include:
- Semantic definitions: Standard business terms and calculations, so “customer revenue” means the same thing across the enterprise.
- Logical models: Domain-oriented data abstractions that hide system complexity while preserving meaning.
- Valid relationships: Defined rules for joining data to prevent invalid combinations and ensuring integrity.
- Physical mappings: Links to actual data sources that allow efficient access without exposing implementation details.
- Data quality constraints: Explicit validation rules to prevent bad data from propagating.
- SLAs and guarantees: Performance, freshness, and availability commitments are tailored to business needs.
Data contracts prevent the usual excuses – no more “we changed the schema and didn’t tell anyone” or “that data isn’t accurate, but it’s what we’ve got.” They provide a foundation for automation, ensuring governance is an integrated part of the system rather than an afterthought.
Data access layer: The connective tissue
Even with well-defined data products and contracts, access remains a challenge. Organizations struggle with fragmented storage, inconsistent security controls, and different query languages for different systems. A unified data access layer solves this by enforcing contracts while enabling flexible, scalable access.
Key capabilities of a data access layer:
- Universal source support: This includes relational databases, document stores, time-series databases, real-time streams, and more.
- Query composition and aggregation: The ability to join and filter across domains without violating business rules.
- Validation enforcement: Both passive monitoring and active enforcement of data quality rules.
- Security and access control: Consistent enforcement of governance policies across all data sources.
- Flexible access methods – REST APIs for developers, SQL for analysts, GraphQL for dynamic queries, and real-time streams for event-driven use cases.
A proper data access layer eliminates bottlenecks, making high-quality data available where and when it’s needed – without compromising security or governance.
The takeaway: Start with access, scale from there
Most organizations try to fix data by reorganizing or centralizing it. That rarely works. A better approach is to start with access – making data discoverable, enforceable, and usable without requiring massive system overhauls.
By implementing a data access layer backed by data products and data contracts, financial institutions can:
- Accelerate product and analytics development
- Reduce regulatory risk through automated governance
- Improve data quality with enforceable contracts
- Lower costs by reducing duplication and manual reconciliation
This isn’t another buzzword-driven transformation effort. It’s a practical, incremental approach that delivers immediate value and builds a foundation for long-term success.
Up next in Part 3 – how to put this all into action, from organizational alignment to selecting the right technology stack.
If you missed Part 1 of this data management series, check it out!
Read Part 1
