The challenge
Your data quality is inconsistent. No one knows who owns what. There’s no governance model, and every team has built their own workarounds. AI can’t scale on unreliable data. Neither can analytics or reporting.
Our approach
We establish clear ownership and stewardship structures, so every critical dataset has someone accountable for it. We define data quality and lifecycle standards that work in practice, not just on paper. We build policy libraries and governance council structures that fit your organisation.
What’s included:
- Ownership and stewardship models
- Data quality standards and controls
- Governance councils and decision structures
- Catalogue and lineage foundations
- Enterprise data modelling and domain structures
- Minimum viable data governance framework
- Governance operating model
- Data governance automation patterns
- Governance architecture
- Data culture enablement
What you leave with:
- Complete data governance operating model
- Trusted data foundations for AI and analytics