Data maturity · Data governance
Your data maturity measured, theme by theme, and turned into a roadmap.
10 themes, a 5-level scale. And the action that moves each level to the next.
The framework’s 10 themes, already written from L1 to L5. One company, one business unit, or 300 at once.
Data maturity · Data governance
10 themes, 5-level scale.
Nordhavn Industries
53 / 100
They measure their maturity with Datamensio
An example
This could be your situation.
Take one company as an example: three sites, three spreadsheets, no shared answer.
Nobody can consolidate.
Nordhavn Industries, 2,400 people in Hamburg, Lyon and Porto. A client asks where the group stands. Each site answers in its own spreadsheet, with its own scales.
Three weeks, a single base.
One Data governance framework (DAMA-DMBOK, ISO/IEC 38505 principles) assessment launched across all three sites at once, from the managers’ interview notes. The framework was already written, its 10 themes and levels L1 to L5 too.
Two costs avoided before being committed.
A score of 53 out of 100, with the gap concentrated on three themes. The AI companion spotted that two actions duplicated those of another audit. The committee report took one sentence to request.
What it saved them
- 3sites measured on the same base, instead of three questionnaires to reconcile
- 2duplicate actions caught before the spend
- 1committee report, with no manual rework
These figures are an example. They could be yours.
The standard imposes processes. Datamensio says where you stand.
01
The framework is already written
Themes, questions and levels L1 to L5, all written. You do not start from an empty spreadsheet.
02
The score lands the same day
Online, by self-assessment link or in interview. Theme by theme, comparable over time.
03
The gap becomes a costed plan
Every step up carries its action. The AI prioritises on expected effect, not on the order of the standard.
04
Progress can be demonstrated
Campaign after campaign, against your target and against your own past. That is what your board asks for.
The maturity scale
One level, the next, and the action that links the two.
It is this mechanism, a level, a level above, and the action linking the two, that turns a finding into a trajectory.
Is the quality of critical data measured and tracked over time?
- N1
No quality measurement. Anomalies are discovered by end users, at the point where a report becomes unusable.
- N2
Checks exist on a few applications, at the initiative of technical teams. Results are not consolidated or shared with the business.
- N3
Quality rules are defined for identified critical data, measured periodically and reported to a named owner.
- N4
Quality indicators are tracked continuously, with thresholds, and anomalies trigger remediation traced back to the source.
- N5
Rules and thresholds are reviewed as usage evolves, remediation is built into business processes, and the effect of corrections on downstream uses is documented.
Action to move from level 2 to level 3
Select the ten most critical data items with domain owners, formalise two to three measurable quality rules for each, and add the reporting of these indicators to the agenda of the quarterly data committee.
« With Datamensio, we meet our objectives far more efficiently. The ERDF inspection services and our supervising ministry particularly appreciated an approach that gives them reliable data. »
Director, CCI 94CCI Île-de-France
« We believe this is the most suitable solution to scale our transformation project and measure impact according to our needs. »
Maja SucekChief Operating Officer, Interreg Danube
Rarely on its own
Frameworks combine. Put several together to cover your business, or have the AI write yours.
Take your first measurement
What this framework covers
Data governance covers the roles, rules and processes that determine how an organisation produces, describes, controls and uses its data. It spans data assets and their description, quality and its measurement rules, data owner and data steward responsibilities, lifecycles, access and sharing, and the architecture that makes data available. Data maturity adds a further dimension: not just what exists, but how repeatable, measured and improved these practices are.
In practice, the topic resists governance because it sits between the IT department and the business, and no one holds the whole picture. Are the organisation’s critical data identified and named, or does each department work from its own list? Is quality measured by indicators tracked over time, or discovered when a report turns out to be wrong? Are data owner and steward roles held by named people with dedicated time, or do they only exist in a charter no one reads any more?
One confusion comes up repeatedly: data governance is not personal data protection. GDPR imposes obligations on a specific category of data and a specific use. Governance covers the whole data estate, including industrial, financial and product data, and its value in use. The recent shift towards decentralised architectures, with business domains responsible for their own data products, does not remove the need for shared rules: it shifts it towards federated standards. Many organisations have adopted domain vocabulary without appointing the corresponding owners.
This assessment does not end in a binary verdict. It places each theme on a five-level maturity scale, described through observable practices rather than adjectives. The gap between the level observed and the target you set generates the action plan, with cost, timeframe and expected impact on the score. The AI groups these actions into a prioritised roadmap, avoiding an unordered list of 80 recommendations.
The framework is ready to use and belongs to you. You can adjust the themes, rephrase the questions, redefine the levels, or ask the AI to build a variant from your data policy and architecture documents. Benchmarking across business units and against your own past assessments lets you track the trajectory, not just the current state.
Reference standard: Data governance framework (DAMA-DMBOK, ISO/IEC 38505 principles)
The themes assessed
Data strategy and value
Existence of an explicit data ambition, alignment with corporate strategy, prioritised use cases, measurement of value produced, dedicated budget.
Organisation and roles
Appointment of data owners and stewards, decision-making bodies, arbitration between domains, time genuinely allocated to these roles.
Data assets and documentation
Inventory of critical data, business catalogue and glossary, shared definitions, end-to-end traceability of data flows.
Data quality
Formalised quality rules, tracked indicators, remediation arrangements, responsibility for corrections at source.
Reference and master data
Identification of shared reference data, creation and modification rules, duplicate management, synchronisation between applications.
Architecture and platforms
Integration methods, storage and processing foundation, pipeline industrialisation, capacity to serve analytical and operational uses.
Access, sharing and protection
Authorisation policies, classification, internal and external sharing conditions, alignment with personal data protection obligations.
Lifecycle and retention
Archiving and deletion rules, management of history, control over the proliferation of copies and extracts.
Analytical use and AI data supply
Quality of datasets used for models, documentation of sources, reproducibility, management of training data.
Culture and skills
Business awareness, training, adoption of self-service tools, ability of teams to reason with documented data.
A short version of the framework is available for the online self-assessment.
Frequently asked questions
Does this assessment lead to certification?
No, and the subject does not lend itself to it. There is no certifying body for data governance. The assessment measures the maturity of your practices and produces the roadmap for your data programme.
How does this differ from a conventional data audit?
An audit lists gaps against an expected standard. The assessment places each theme on a progressive scale and indicates the action that moves you to the next level. You get a trajectory, not just a finding.
How long does the assessment take?
The short version can be completed in a single session. The full version, run collaboratively with domain owners and architecture leads, typically spans one to two weeks, with most of the time spent gathering input from contributors.
Can the framework be adapted to our data organisation?
Yes. Themes, questions and levels can all be modified, and you can start from a blank base. The AI can also build a variant from your data policy or your application map.
Does answering require a technical profile?
The questions cover organisation, rules and practices, not configurations. A data manager or a business director can answer them. Architecture questions can be assigned to a technical contributor in collaborative mode.
How can several business units be compared?
Each entity is assessed against the same framework, making scores comparable by theme. A cross-entity roadmap consolidates the assessments and groups common actions rather than duplicating them entity by entity.
Does this assessment cover personal data protection?
It covers access, classification and sharing conditions, in line with your obligations. It does not replace a dedicated personal data protection assessment, which falls under a separate framework.
Where is the data hosted?
In France, with OVH, with backup at Scaleway. No transfer outside the European Union. The AI models used can be selected, including from European solutions.