What is Data Governance?

Data Governance is the framework — policies, procedures, decision rights, accountabilities — that ensures enterprise data is available, usable, consistent, trustworthy, and compliant with regulatory obligations. It operates at the intersection of IT, Legal, Risk, and business units.

Reference bodies of knowledge: DAMA-DMBOK (Data Management Body of Knowledge) is the industry-standard framework covering 11 domains — Governance, Architecture, Modeling, Storage, Security, Integration, Document Content, Reference & Master Data, Data Warehousing, Metadata, and Quality. DCAM (EDM Council's Data Management Capability Assessment Model) is more assessment-oriented. CDMC extends to cloud-specific controls.

Core building blocks: (a) Governance body — Data Governance Council, typically chaired by a Chief Data Officer; (b) Policies — written rules (classification, retention, quality SLAs, access); (c) Standards — how policies are implemented (naming conventions, tagging schemes, SLAs); (d) Roles — Owner (accountable business figure), Steward (responsible for day-to-day), Custodian (IT executor); (e) Processes — change management, issue management, access provisioning, audits.

Maturity models chart progression: ad hoc → managed → defined → measured → optimized. Most organizations are at 'managed' and aim for 'defined' — ad hoc to defined is the bulk of the work and the biggest cultural shift.

Common failure modes: governance-by-committee (100 meetings, 0 decisions), over-centralization (bottleneck the CDO office), regulation-driven (we only fix what auditors catch), technology-first (buying a catalog before defining policies).