Problem
01
We do not trust our own numbers.
02
No one owns the data.
03
We cannot meet our regulatory obligations on data.
Outcomes
- Trusted, governed data
- Clear ownership
- A defensible compliance position
Scope
In scope
- Data maturity assessment
- Governance and ownership model
- Data quality controls
Not included
- Building the platform itself, which sits under data engineering
- Model governance, which sits under responsible AI
Approach
Discovery
We agree the problem, the data and the constraints, and how success will be judged.
Delivery planning
We shape the approach, sequence the work and set out costs, risks and controls.
Build and test
We build in the open, test against real data and cases, and prove it before it goes near production.
Operational readiness, hypercare and closure
We hand over, support the first weeks of use, and confirm the outcome before we close.
Deliverables
Data strategyGovernance and ownership modelData quality framework
Technologies
Data management frameworks (DAMA-DMBOK as a reference)Cataloguing and lineage toolingData profiling tooling
Controls
Every priority dataset has a named owner. Access to personal data is role-based and recorded. Retention periods are set and applied. Definitions are version-controlled and published.
Frequently Asked Questions
Is this just a policy document?
No. It includes the ownership, controls and roadmap to make the policy real.
Do we need this before doing AI?
In most cases yes. Trustworthy data is what stops an AI project failing quietly.
Ready to scope this?
Tell us where your data lets you down and we will help you make it dependable.