Data maturity baseline
Assessment across quality, lineage, ownership, accessibility, classification and controls.
AI Services
Data Maturity and Governance Evaluation
Assess whether your data can support the AI you are planning — quality, lineage, access, ownership and controls — and fix what blocks it.
What it is
AI exposes data problems that reporting tolerated for years. A dashboard with 8% missing values is a footnote; a model trained on the same data makes that gap into a systematic bias.
We assess data quality, lineage, ownership, classification, access controls and privacy posture against the AI use cases you intend to build — then separate what must be fixed first from what can improve over time.
Who it is for
- Organizations whose AI plans depend on data nobody owns
- Data and privacy leaders who need AI-specific controls assessed
- Companies with data spread across systems that disagree with each other
- Teams needing an evidence-based case for data governance investment
What is included
The deliverables below are the default scope. We adjust them to your situation during scoping and confirm the final list in writing.
Use-case data readiness
Per intended AI use case: what data it needs and whether that data is fit for the purpose.
Ownership and stewardship map
Who owns each critical data domain today, and where ownership is genuinely absent.
Access and privacy review
Who can reach sensitive data, how it is classified, and what that means for AI use.
Remediation plan
Blockers first, improvements second, with effort bands and suggested sequencing.
Process and timeline
Indicative timings for a typical engagement of this type. Your scope, environment and availability of stakeholders will move them.
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Scope by use case
Identify the data domains your AI plans actually touch.
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Profile and assess
Data profiling, lineage tracing and interviews with stewards and system owners.
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Governance review
Assess existing policy, classification and access controls against AI needs.
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Plan and report
Findings, remediation plan and readout.
Outcomes we aim for
These are the goals we design the engagement around. They are objectives, not guarantees, and the result depends on decisions and effort on your side as much as ours.
- A clear view of whether your data can support the AI you are planning
- Named owners for the data domains that matter most
- Access and classification gaps identified before a model touches sensitive data
- Remediation scoped to unblock value rather than to reach a maturity score
Questions
About data maturity and governance evaluation
Do we need a data lake or warehouse first?
Not necessarily. Several useful AI use cases run on data already sitting in operational systems. A consolidation program is sometimes the right answer, but it should be justified by specific use cases rather than assumed.
How does this relate to our existing data governance program?
We extend it rather than replace it. If you already have stewardship and classification, we assess whether they cover what AI introduces — training data, model outputs, inference logs and retention.
Is data maturity and governance evaluation the right next step?
Tell us the symptom rather than the solution. If a different service fits better — or none of ours does — we will say so.