Data Governance

Data Governance is the set of policies, processes, roles, and standards an organization adopts to ensure its data is accurate, secure, compliant with regulations, and used consistently.

How it works #

It defines who is responsible for data (data owner, data steward), what quality rules to apply, how to classify data by sensitivity, and how to trace its provenance (data lineage). In an AI context, it also includes verifying the provenance and quality of data used for model training.

What it’s for #

Without data governance, an organization doesn’t know what data it has, where it is, who can access it, and whether it’s reliable. In projects with AI components, governance is the prerequisite for preventing models from being trained on dirty, unauthorized, or regulation-bound data such as GDPR-protected information.

Why it matters #

In every AI project in a regulated environment, the Governance-Compliance-Automation triangle must stay balanced. An efficient AI automation that violates data governance policies is a risk. Perfect governance that blocks all automation stalls the project. The AI Manager keeps these three vertices in balance.