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 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
