Regional Financial Services Firm
Enterprise data governance program reduced regulatory findings by 85% and enabled self-service analytics
A regional financial services firm with $12B in assets under management faced mounting regulatory pressure over data management practices. We designed and implemented a comprehensive data governance program that resolved compliance gaps and unlocked self-service analytics for the business.
The Challenge
The firm had received multiple regulatory findings related to data lineage, access controls, and data quality in its risk reporting. The existing approach to data management was informal — data ownership was unclear, there were no documented data quality standards, and sensitive customer data was accessible to users who did not need it for their roles. The CISO and Chief Data Officer were under pressure from the board to resolve the regulatory findings within 12 months, while also enabling the business teams who were requesting faster access to analytics.
Our Approach
We approached this as both a governance and an enablement challenge — the goal was not just compliance but building a data capability that made the firm more competitive.
We started with a data catalog initiative, inventorying all critical data assets across the firm's core systems — the portfolio management platform, trading systems, client reporting, compliance monitoring, and the data warehouse. We documented data owners, data definitions, quality expectations, and access requirements for each asset.
We then designed and implemented a three-tier governance framework:
Tier 1 — Regulatory critical data (client PII, financial reporting data, risk metrics): Strict controls including automated lineage tracking, quality monitoring with alerting, formal access approval workflows, and quarterly audits.
Tier 2 — Business critical data (portfolio analytics, performance attribution, market data): Documented ownership and quality standards, role-based access controls, and semi-annual reviews.
Tier 3 — Operational data (internal reports, departmental analytics): Self-service access within defined guardrails, basic quality monitoring, and annual review.
This tiered approach focused governance effort where it mattered most while enabling self-service access for lower-risk data.
We implemented the technical infrastructure to support this framework: a data catalog (Collibra), automated data lineage tracking, quality monitoring with dbt tests, and role-based access controls integrated with the firm's identity management system.
Finally, we built a self-service analytics layer using Tableau with semantic models that gave business users safe, governed access to the data they needed without requiring them to write SQL or understand the underlying data architecture.
The Results
Regulatory findings reduced by 85% — the next examination resulted in only 2 minor findings compared to 13 in the previous cycle. Examiners specifically noted the data governance program as a model practice.
Data lineage documented for 100% of regulatory reports — automated lineage tracking from source systems through transformations to final reports, available on demand for auditors.
Self-service analytics adoption — 120 business users across portfolio management, risk, and client service now access analytics directly, reducing the data team's ad-hoc request volume by 70%.
Data quality issues detected 5x faster — automated monitoring catches quality problems at ingestion, with average detection time reduced from days to minutes.
Access control compliance — role-based access controls now cover all Tier 1 and Tier 2 data assets, with quarterly access reviews automated and tracked.
Scalable framework — the governance program is designed to scale as the firm grows through acquisition, with clear onboarding procedures for integrating new data sources.
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