Private Sector

Problem A mid‑sized private equity firm lacked a formal data cataloging capability and had no centralized view of its business terminology, system metadata, dataset structures, or column‑level definitions. Teams relied on ad‑hoc knowledge, spreadsheets, and tribal understanding of data, which created inconsistencies in reporting, slowed analysis, and made it difficult to answer even basic data questions. The organization had also never defined what qualified as a Critical Data Element (CDE), resulting in inconsistent prioritization and unclear ownership. To move toward stronger governance and future data quality monitoring, the firm needed a lightweight but structured cataloging approach that could be adopted quickly without enterprise tooling. Approach Outcome

Problem After establishing a foundational Data Catalog for Critical Data Elements (CDEs), the private equity firm still lacked a way to measure the reliability of those elements. There were no automated data quality checks, no monitoring process, and no structured way to identify or resolve data issues. As the business began using the catalog, they realized they needed a repeatable mechanism to validate the accuracy, completeness, and consistency of the CDEs they depended on for investment analysis, financial reporting, and portfolio insights. The organization needed a lightweight, scalable Data Quality Program that aligned with the CDEs defined during the catalog enablement. Approach Outcome

Problem A large financial services organization needed a clear understanding of its current Data Governance maturity, specifically within the Metadata Management and Data Quality Management workstreams. While the company had multiple data initiatives underway, leadership lacked visibility into: The organization requested a structured, objective maturity assessment to evaluate current-state capabilities and provide a clear path toward a target-state operating model. Approach You conducted a comprehensive maturity assessment across the Metadata Management and Data Quality Management domains, using a structured framework aligned to DAMA, DCAM, and industry best practices. The assessment included interviews, artifact reviews, system evaluations, and process walkthroughs. Outcome