Data Governance

With a domain that has a concrete problem (regulatory reporting, a disputed metric, customer data) and someone who suffers from it. We inventory the domain’s critical data, name an owner per dataset, write definitions and quality rules, then tool them in a catalogue (DataHub, OpenMetadata, Collibra or Purview depending on your context). The general framework is derived from that first real scope, not the other way round.

By automating rules rather than multiplying committees: sensitive data classified at ingestion, quality checks in CI, mandatory metadata verified at deployment, access granted by policy rather than by ticket. A small number of non-negotiable rules enforced by the platform, and everything else left to the domains’ autonomy. That is the spirit of Data Mesh federated governance.

For high-risk systems it requires documenting training and evaluation data, their quality and provenance, and keeping decisions traceable. Technically: end-to-end lineage, versioned datasets, archived evaluations, inference logs. We prepare these elements with your compliance teams; the legal qualification of the system remains theirs.

With evidence produced by the platform rather than statements: lineage from source to report, versioned quality rules with historised check results, named owners, access history. An auditor who can trace a figure back to its source in a few clicks asks far fewer questions.

This is where governance pays off directly: an up-to-date catalogue lets an assistant find the right table and understand a column; well-set access rights stop an agent from reading what it should not; data contracts keep a vector index from being fed stale documents. Organisations that invested in these foundations move from demo to production much faster.