Problem A government agency was operating on an aging Precisely Data Quality platform that no longer met its scalability, automation, or governance needs. The tool was costly to maintain, difficult to extend, and lacked modern capabilities required for enterprise‑level data quality monitoring. The agency selected Ataccama as its next‑generation Data Quality platform. Approach Outcome
Problem A large financial services organization lacked a centralized data catalog and had no enterprise‑wide visibility into its technical metadata, business metadata, business glossary, data governance policies, standards, or data lineage. Without a cataloging platform, teams relied on tribal knowledge, manual documentation, and inconsistent definitions. During the implementation of Alation as a SaaS solution, the organization also discovered significant gaps in foundational metadata: no authoritative inventory of data assets, unclear ownership and stewardship assignments, inconsistent lifecycle statuses, and missing connection details. These gaps surfaced as the team attempted to onboard data assets into the catalog. Approach Outcome
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 major financial services organization needed a scalable, automated way to monitor the quality of prioritized data assets across its Azure Data Lake. Data quality checks were inconsistent, issue identification was manual, and business units had limited visibility into the health of the data they produced or consumed. Without centralized monitoring, data issues often surfaced late and required significant effort to diagnose and resolve. 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
