A practical bank data governance operating model
Explore how ownership, definitions, quality checks, and operating routines can support banking data governance. This illustrative scenario explains a proposed Cicrim approach. It does not describe a verified client engagement or measured client results. Benefits would need to be evaluated against the institution’s own baseline.
Illustrative scenario
A bank pursuing analytics, reporting, and AI needs dependable data definitions and accountable ownership across business and technology teams.
The business challenge
Data issues can persist when teams disagree about definitions, authority, or the process for resolving defects.
A governance committee needs practical workflows and usable evidence to connect policy with everyday data decisions.
Proposed approach
The proposed approach clarifies ownership, defines priority data elements, and embeds quality review and issue resolution into operating routines.
Capabilities to consider for a controlled implementation:
- Ownership: Assign accountable business owners and operational stewards to priority data domains.
- Definitions: Agree on critical terms, approved uses, sources, and lineage.
- Quality controls: Set relevant checks, escalation criteria, and resolution expectations.
- Operating cadence: Review material issues, dependencies, change requests, and decisions.
Benefits to evaluate
- Definition consistency: Evaluate reconciliation effort and repeated interpretation disputes.
- Data quality: Track defects, recurrence, and downstream impact in priority workflows.
- Accountability: Assess issue age, owner clarity, and evidence of resolution.
- Delivery readiness: Check whether reporting and AI teams can use approved data with known limitations.