Data governance
The set of decisions, roles and controls that determine who can do what with which data, and who is accountable when it goes wrong. It is not a compliance project you finish once.
What it covers
In the Rodan Data Governance Toolkit we score six domains: ownership, inventory, quality, access, lifecycle and privacy. Together they answer one question: can we lawfully use and trust this data?
Good governance enables trustworthy reporting, defensible AI adoption, faster deals and regulatory resilience. Weak governance shows up as metric drift, vendor sprawl and AI deployed before classification.
What it is not
It is not buying a catalogue tool, running a one-off audit or appointing a committee that cannot stop non-compliant projects. Governance only counts when it changes behaviour.
Score yourself
Take the governance maturity self-check (~30 min, runs in your browser) or browse the governance toolkit with sector overlays and working templates.
Related terms
- Data maturity: can you reach, join and analyse the data you have governed?
- Commercial insights: can you turn trusted, reachable data into decisions?
- Data catalogue: where governed data is found, described and requested
- Data quality: whether data is fit for the use it is put to
- Data steward: who makes day-to-day decisions about a set of data
- Data classification: the sensitivity labels that drive handling rules
- Data retention: how long data is kept and how it is deleted
- Single source of truth: one agreed definition and owner for each metric
- Data mesh: domain ownership held together by federated governance