Data Governance

Data governance is the foundational discipline of deciding who decides about data — who owns it, who can use it, what it means, what quality it must hold to, where it lives, how long it stays, and how the organisation defends those decisions to regulators and auditors. It sits at the intersection of records management, the DAMA DMBOK and EDM Council DCAM frameworks, the modern data-mesh and data-product movement, the metadata / catalog / lineage stack, the data quality and observability stack, master and reference data management, security and privacy governance, lifecycle and retention, the regulatory landscape (GDPR, CCPA, HIPAA, BCBS 239, SOX, EU AI Act), and the modern AI / ML extension that turns DG into a prerequisite for any serious AI deployment. Over the last decade it has stopped being a back-office function and has become a board-level operating commitment subject to regulator inspection, material fines, and procurement scrutiny.

This track is written for the practitioners doing this work day to day: chief data officers, data governance leads, data stewards, data product owners, data engineers, analytics engineers, data scientists, ML engineers integrating DG controls, privacy and security partners, lawyers and compliance leads, and the cross-functional partners who make DG land. Every topic explains the underlying discipline (drawing on DAMA DMBOK 2, the EDM Council DCAM, ISO 8000, BCBS 239, the OpenLineage / OpenMetadata / DataHub specifications, the canonical research literature on data quality and entity resolution, regulator guidance, and hard-won production experience), the practical methodology that operationalises it, the artefacts and rituals that make it stick, and the failure modes where data governance work quietly fails to govern anything. The aim is that a reader can stand up a credible data governance function, integrate it with engineering, business, and regulatory partners, and defend it to boards, auditors, and customers.