Ownership & stewardship
Clarify who owns critical data, who maintains it and how issues are escalated.
Data Governance
Make critical data accountable, reliable and governed.
Establish practical governance around your critical data, with clear ownership, stewardship, quality controls and measurable remediation.
What we address
A foundation your organisation can rely on.
AI assurance depends on the quality and accountability of the underlying data. We help connect data governance policies with day-to-day responsibilities and controls.
Quality & control
Identify critical data elements, define validation rules and establish issue management.
Operating model
Set governance forums, decision rights, policies and management reporting.
Engagement outputs
Specific deliverables.
A clear next step.
Scope, evidence requirements and deliverables are agreed before work begins. Each engagement is shaped around your systems, risk exposure and current maturity.
- Maturity and gap assessment
Baseline the current governance arrangements and priorities.
- Governance operating model
Define responsibilities, decision rights and escalation routes.
- Stewardship and control requirements
Specify practical controls and responsibilities for critical data.
- Implementation roadmap
Prioritise actions, dependencies and measures of progress.
Common questions
A clear basis for engagement.
Where do we start?
Start with a maturity assessment or a defined critical data domain. The scope can expand as ownership, controls and evidence improve.
How does this connect to AI?
Data quality, provenance and permitted use shape AI risk. We connect those requirements to AI governance and assurance where they are relevant to the agreed scope.
Make governance operational.
Discuss your data governance, AI assurance or agentic AI control requirements with Data Angles.