data-governance

Once a system is high-risk, Articles 8 to 15 set out what it must satisfy. This lesson works through them as engineering requirements rather than legal text: risk management as a continuous process, data governance including the 2026 change on special category data for bias detection, human oversight as a design property, accuracy and robustness, and transparency toward the deployer.

Governance must protect data as well as make it usable. Learn the control layer: how data classification by sensitivity drives every protection, how access control and least privilege limit exposure, how privacy by design reduces risk, how policies, standards, and procedures fit together, and why the best governance enables trusted self-service rather than locking data away.

Governance is accountability, and accountability needs names. Learn the core data governance roles, owner, steward, and custodian, and exactly who does what, then the operating models (centralized, decentralized, federated), why heavy committees fail, and how domain ownership and data-as-a-product push accountability to the teams closest to the data.

Data governance is one of the most misunderstood functions in business: dismissed as bureaucracy, confused with IT or privacy law, rarely explained clearly. Learn what it actually is (managing data as a business asset through accountability and decision rights), the real cost of not doing it, why most programs fail as bureaucratic theater, and what the working version looks like.

Governance policy becomes real through concrete machinery. Learn the working parts every data governance program relies on: the dimensions that define data quality and how to measure them, metadata and catalogs that make data findable, lineage that traces where data came from, master data management that creates a single source of truth, and data contracts that push quality upstream.