For CEOs
Data for CEOs: the no-jargon guide
Everything a CEO needs to know about data to make better decisions, without the technical complexity: what to ask for, what to measure and how to get results.
Read articleKey takeaways
- Maturity models describe a company’s evolution in using data.
- They usually define five levels, from reactive to optimised.
- Identifying the current level helps set realistic goals.
- Skipping levels rarely works: maturity is built in stages.
- AI on an immature base is a recipe for failure.
Not all companies are at the same point in their relationship with data, and trying to implement advanced AI on an immature base is a recipe for failure. Data maturity models offer a map to locate yourself and plan progress.
What it is
A data maturity model describes the levels an organisation progresses through in its ability to manage and exploit data, from reactive, manual use to optimised, AI-based exploitation.
The five levels
- Reactive: scattered data, manual reports, gut-feel decisions.
- Aware: first dashboards, but no governance or single source.
- Defined: centralised layer, quality and reliable reporting.
- Managed: mature governance, self-service, advanced analytics.
- Optimised: decisions and processes powered by AI on governed data.
Reactive
ManualGut feel
Defined
Central layerQuality
Optimised
AI ongoverned data
How to locate yourself
Honest questions place you: how long to get a report? do figures match across systems? who can access data without technical help? is AI used on real, governed data?
Advancing without skipping stages
The temptation to jump straight to "optimised" with AI, without solving quality and governance, explains many failed projects. Maturity is built in stages: first a reliable data layer, then governance and self-service, and only then AI on that base.
Implementing advanced AI on an immature data base is one of the most common reasons projects fail.
In summary
Data maturity models map the journey from reactive, manual use to AI-optimised decisions across five levels. Locate yourself with honest questions, set realistic goals and advance in stages — first a reliable layer, then governance, then AI. Skipping levels is why many AI projects fail.
Sources & further reading
Frequently asked questions
What is a data maturity model for?
To locate the organisation in its evolution, set realistic goals and plan investments in the right order.
Can levels be skipped?
Rarely. Implementing advanced AI without quality or governance usually fails. Maturity is built in stages, each enabling the next.
How do I know my level?
By answering questions about reporting time, cross-system consistency, self-service and AI use on governed data.
What are the five levels?
Reactive, aware, defined, managed and optimised — from scattered manual data to AI on governed data.
Why is jumping to AI risky?
Because AI on scattered, ungoverned data produces unreliable results. The base must be reliable and governed first.
What comes before AI?
A reliable, centralised data layer and then governance and self-service. AI is the final stage, built on that foundation.
Turn this data into results
Tell us what you want to achieve. Data Layer connects, processes and delivers the result up and running, with no infrastructure for you to manage.