Client background
A bank expanding analytics and AI initiatives discovered that inconsistent data definitions, unclear ownership, and weak controls were slowing delivery and creating risk. Leadership needed a governance model that supported regulatory expectations without overburdening teams.
The business challenge
Critical data elements were defined differently across business lines, and data quality issues were discovered late—after reports were produced or models were built.
The bank needed clear ownership, definitions, and control processes that could scale with new use cases, vendor feeds, and modernization efforts.
Strategy and solution
Cicrim designed a lightweight but disciplined governance operating model that clarifies ownership, standardizes definitions, and embeds controls into real workflows.
The solution included designing and deploying a set of capabilities aligned to the institution’s operating model:
- Critical data element program: Identified priority data domains and established definitions, owners, and quality rules.
- Governance operating model: Defined roles, decision rights, workflows, and escalation paths that fit the bank’s structure.
- Quality controls and monitoring: Implemented repeatable checks and dashboards to measure quality and track remediation.
- AI readiness alignment: Mapped governance artifacts to AI and reporting needs so teams move faster with less rework.
This solution led to the following key benefits
- Improved consistency: Standardized definitions reduced reconciliation and rework across teams.
- Higher data quality: Earlier detection of issues improved downstream reporting and model reliability.
- Clear accountability: Assigned ownership and decision rights for faster resolution of data conflicts.
- Faster delivery: Reduced friction for analytics and AI projects by stabilizing the data foundation.