Data Retention Compliance Framework for a Global Financial Services Leader
Overview
A leading global financial services provider encountered serious data compliance gaps in managing data retention across its corporate and commercial business lines. Our client was exposed to regulatory risks and audit findings when the Commercial Data Office (CoDo) found that 350+ structured datasets lacked proper record classification, specified retention periods, and comprehensive purging procedures.Â
Solution
To categorize, retain and purge data in accordance with the data retention compliance policy for audit readiness, our team created a 7-step framework while mitigating AI/ML security and governance risks:Â
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- Table Selection and Scoping: Based on business needs and compliance risk, datasets were selected from operational and ingestion platforms.
- Record Declaration: Each dataset was assessed according to business purpose, source authority, downstream dependencies, and legal requirements using a structured decision tree created with Global Records Management (GRM). Tables were categorised as either non-records, which required instant deletion, or official records, which required retention and purging for further data lifecycle management.Â
- Retention Schedule Mapping: To define retention period, triggering events, and geographic applicability, official records were mapped to particular Record Classes inside the enterprise-wide Record Retention Schedule (RRS). All mappings required validation with legal terms and official GRM approval.
- Purge Logic Definition: Specific business logic was created for each official record to ensure compliance with jurisdiction-specific regulations. With this step, data that surpassed retention periods according to the data retention compliance were identified using event date columns or surrogate logic where necessary.
- Downstream Notification & Alignment: Affected teams received structured notifications outlining the data that will be deleted, implementation schedules, and co-ordination requirements to ensure operational continuity prior to any purge execution.
- Controlled Execution: Data Correction Utility (DCU) for one-time operations and Data Lifecycle Management (DLM) for ongoing automation were used to carry out the purge. Both technologies offered rollback possibilities, market-specific controls, and audit trails.
- Documentation and Closure: All record retention schedules, classification, purge logic, approvals, and execution logs were uploaded to centralized repositories for future reference.Â
Impacts Delivered
To eliminate audit findings and reduce regulatory exposure for higher levels of enterprise data governance:
- More than 350 datasets were categorised and brought into compliance with the business’s data retention policy
- Automated purging through Data Lifecycle Management created repeatable workflows for continuous compliance with the data governance policy
- Cleaner datasets for ML/AI projects and improved analytical dependability
- Scalable methodology for comparable compliance issues in the future on different platforms and business lines.
- Reduced storage costs and computational resources for processing and backup operations.
