Enterprise data management (EDM) is the strategic, organization-wide practice of handling data as an asset across its full lifecycle, so it stays consistent, secure, and accessible to the people and systems that need it. It spans data governance, integration, quality management, master and metadata management, architecture, and compliance.
The goal is one trusted view of data across the business. Without it, teams work from conflicting copies, definitions drift between departments, and analytics and AI inherit those inconsistencies. EDM sets the policies, standards, and ownership that keep the estate coherent at scale.
EDM keeps the estate governed and consistent, but it does not, on its own, capture the exact data state a specific AI run executed on or make that run reproducible. Getting a dataset into an AI-ready, reproducible state for a given model is a distinct layer that sits on top of enterprise data management.