• Data as a system, across every product line. A consistent, governed view of funds, entities and positions that holds up wherever it is consumed — operated vehicles, administered funds, and client-held positions alike.
• Fund and entity data quality — the security master. Fund and manager records, entity resolution and duplicates, fund properties and characteristics.
• Data quality and completeness. Design and run the checks that validate data before it reaches clients, and the exception workflows behind them.
• Reconciliation. Reconcile data across our systems and sources, and explain the differences clearly.
• Root-cause analysis. Trace a wrong number back to its source, size the problem, and specify the fix.
• Extraction quality. Expand the data we capture and evaluate how well our tooling captures it.
• Documentation. Definitions, assumptions and edge cases, written down and kept current.