Open Data Infrastructure
Open Data Infrastructure for Financial Services and Banking
Why regulatory reporting, auditability, and risk aggregation push banks toward portable, governed data foundations.
If your risk team cannot explain where a number came from, the conversation with regulators gets bad fast.
Why it matters
Financial services needs strong controls around data quality, aggregation, and reporting. It also needs repeatability under stress. That means lineage, audit trails, and clear ownership are operational requirements.
Those requirements are hard to meet when meaning and controls live inside one vendor boundary that cannot be audited across the full data path.
The ODI angle
ODI helps by making the contract explicit: open storage, consistent catalog semantics, and policy enforcement that can be audited.
BCBS 239 is a useful lens here. It is not a tooling recommendation. It is a statement about capabilities around risk data aggregation and risk reporting.
If you can move compute and still preserve contract and controls, you have options without losing governance.
Core idea: auditability is a feature of architecture, not a feature of one vendor product.
The architecture test
For banking data leaders, the test is whether a report can be reproduced and defended.
- Define authoritative sources and encode ownership in the catalog.
- Capture lineage for risk reports and material transformations.
- Enforce access control and segregation of duties in the data path.
- Treat reconciliation and data quality checks as production jobs.
- Design for repeatability and audit trails across tools.
What breaks first
This breaks when controls exist as process instead of system behavior.
- Risk aggregation depends on manual extracts and spreadsheets.
- Lineage is rebuilt after an incident instead of captured continuously.
- Data quality checks are informal and inconsistent across teams.
- A vendor boundary prevents cross-platform auditability.
Questions to ask
Use these questions when you evaluate open data infrastructure financial services for regulated reporting and risk control.
- Can you reconcile risk data to authoritative sources with automation?
- Can you produce an end-to-end audit trail for a report?
- Where does metadata live, and can it be exported and reused?
- What happens when you add a second engine to the stack?
- How do you ensure consistent policy enforcement across tools?
If your answer is "the vendor has a report for that," you are not building a defensible foundation. You are outsourcing control.
Sources to start with
Start with the primary sources on risk data aggregation expectations, then translate them into infrastructure behavior.