Establishing data governance
Ownership, policy and stewardship are the basis. Without governance, every AI application is built on quicksand. We design a governance framework that fits your organisation — pragmatic, not bureaucratic.
Service · AI Enablement
Without a solid data foundation AI stays a promise. We help organisations in the Netherlands move from AI ambition to measurable value — with reliable data, governance and the right technology.
AI Data Readiness is the process of preparing your data foundation for trustworthy, compliant AI applications. cimt delivers this in two steps: first an AI Readiness Assessment that measures the maturity of your data governance, data quality, architecture, metadata and use cases against the DAMA DMBoK framework; then an implementation track that lays the foundations — governance structure, data quality monitoring, lineage with erwin Data Intelligence, and use case prioritisation. Result: AI models on trustworthy data, traceable for the EU AI Act, and tied to business KPIs.
The reality
AI is at the top of every boardroom agenda. But the reality is harder: more than half of all organisations report their data isn't AI-ready. Budgets grow, but projects stall on poor data quality, missing governance and aging architecture.
The EU AI Act — in force since 2025 — adds a legal dimension. Organisations that deploy AI without demonstrable data quality and traceability face compliance risk.
The foundation needs to come first. That is where cimt starts.
Step 1
Before you invest in AI tooling, you need to know where you stand. In 2–4 weeks we map how mature your data landscape is and which steps are needed to deploy AI successfully.
| Component | What we evaluate |
|---|---|
| Data Governance | Ownership, policy, stewardship and decision-making structures |
| Data Quality | Completeness, consistency, timeliness and reliability of core data |
| Data Architecture | Platform landscape, integration paths and scalability for AI workloads |
| Metadata & Lineage | Cataloguing, provenance and traceability — essential for EU AI Act compliance |
| Use Case Prioritisation | Which AI applications deliver measurable value fastest? |
What you get: a concrete report with your current data maturity score, a prioritisation list and a roadmap to AI readiness. Fixed price, fixed timeline, no open-ended commitments.
Step 2
After the assessment we help you lay the foundation that makes AI possible — structured according to the DAMA DMBoK framework.
Ownership, policy and stewardship are the basis. Without governance, every AI application is built on quicksand. We design a governance framework that fits your organisation — pragmatic, not bureaucratic.
AI models are only as good as the data feeding them. We implement data quality management and monitoring so your data stays reliable, complete and current.
The EU AI Act requires traceability. With erwin Data Intelligence we catalogue your data assets, capture provenance and create a complete picture of your data landscape. Not just compliance — the basis for reusable, trustworthy AI.
Not every AI application delivers the same value. We help identify the use cases that produce measurable results fastest and tie them to the data foundations they require.
Our technology
Best-of-breed technology from our strategic partners. Each tool has a specific role in making your data landscape AI-ready.
| Partner | Role in AI enablement | More |
|---|---|---|
| Qlik | Data integration and quality (Qlik Talend Cloud); AI-driven analytics (Sense, Answers, Predict) | View |
| Snowflake | Scalable data platform for AI workloads, lakehouse architecture, Data Vault 2.0 | View |
| erwin by Quest | Data modelling, metadata governance and AI traceability (Data Modeler, Data Intelligence) | View |
Start with the basics
In 2–4 weeks you know exactly where your data stands — and the fastest route to trustworthy, EU AI Act-compliant AI.
Frequently asked
AI-ready data is structured, classified, governed and of known quality. Concretely: a working data catalog, lineage from source to destination, quality monitoring with agreed thresholds, and clear ownership per dataset. Without these foundations, AI models produce unpredictable results and EU AI Act compliance is not demonstrable.
Typically 2 to 4 weeks, depending on organisation size and scope. An SME with a few datasets is done in 2 weeks; an enterprise with dozens of source systems takes 4 weeks. We always deliver: a maturity score per DAMA knowledge area, prioritisation list and roadmap with indicative timelines.
The EU AI Act (in force since 2025) requires that for high-risk AI applications you can demonstrate which data the model used, how that data was produced, and which quality controls ran. Without data lineage and governance that's impossible to prove. Fines reach up to 7% of global revenue — comparable to or stricter than GDPR.
Foundation first. AI tooling on a weak foundation produces disappointing results and has to be rebuilt later. The order that works: assessment (where are we?) → governance + quality (foundation) → use case prioritisation → tool selection → implementation. Every euro of investment then builds on the previous one.
We work alongside your existing tooling. cimt is a Qlik Elite Partner, Snowflake and erwin partner, but the assessment and governance approach is technology-neutral. If your current stack fits your goals we help optimise it; we only recommend replacement when there is a fundamental mismatch.