Data Governance and Enterprise Architecture Consulting
We build the data foundation AI will stand on: ownership, quality, classification and architectural discipline.
Most AI projects stall because of data, not the model. There is data but no owner. There is an owner but no definition. There is a definition but it differs between two systems.
Data governance is the work of writing a name against every link in this chain. It is not a technology project, it is a responsibility project.
The link to AI
ISO 42001 and the EU AI Act tie model quality to data quality. An organization that cannot show the source, representativeness and processing history of its training data cannot defend its model either. That is why data governance is now a prerequisite of AI governance.
How we work
- Data asset inventory and identification of critical data elements
- Ownership and steward assignments
- Data quality rules and measurement thresholds
- Classification and access policy
- Target enterprise architecture and transition plan
Tangible deliverables
- Critical data elements catalog
- Ownership matrix
- Data quality scorecard
- Data classification policy
- Current and target architecture diagrams
- Transition roadmap
Frequently asked questions
Is data governance a software project?
No. A tool helps but does not solve the problem. Organizations that install catalog software without assigning ownership are left with an empty catalog.
Where do you start?
With critical data elements. Projects that try to map the whole organization never finish. Starting with the thirty or so data elements that feed board reports delivers visible results within six months.
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