Data governance — from audit risk to controlled infrastructure

What the client needed to solve

The firm had multiple source systems feeding performance and risk reports with no formal documentation of how data moved between them. Calculations were applied inconsistently. Data owners were unclear. When auditors asked how a number had been produced, the answer was "ask the analyst who built the spreadsheet."

The Investment Committee and board were receiving reports they could not independently verify. This was not a data quality problem — it was a governance problem. The data existed. The controls around it did not.

How the work was structured

• Identified core data sets, golden sources, data owners, and calculation methodologies for all performance and risk data used in reporting
• Documented end-to-end data flows from source systems to final outputs — including every transformation, enrichment, and validation step
• Mapped business processes and the general operating model, identifying control points that could be evidenced for audit
• Defined data quality rules, reconciliation processes, validation procedures, and exception reporting workflows
• Implemented priority data quality and governance changes, including access controls and data management procedures
• Documented reporting requirements for each stakeholder group: Principal, Investment Committee, Management, and Audit and Risk Committee