Maturity of open data strategy and data transparency
Your open data strategy, measured by theme and translated 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.
Maturity of open data strategy and data transparency
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 Open data and data transparency maturity framework (FAIR principles, European Open Data Directive) 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.
This mechanism, a level, the level above, and the action linking the two, is what turns a finding into a trajectory.
Are published datasets documented and kept up to date under an explicit commitment?
- N1
No documentation accompanies publications. Datasets are uploaded as is, without a schema or point of contact.
- N2
Some datasets have a brief description, produced at the initiative of the publishing team. No update frequency is stated.
- N3
A common documentation template is applied to most datasets: schema, variable dictionary, licence, contact and stated update frequency.
- N4
Documentation is checked before publication and freshness is tracked by indicator. Deviations from the update commitment trigger a logged action.
- N5
Documentation evolves with reuser feedback and periodic portfolio reviews. Decisions to update, freeze or withdraw are documented.
Action to move from L2 to L3
Adopt a single dataset record template (schema, variable dictionary, licence, contact, update frequency), make it mandatory before any publication and check its application during the quarterly catalogue review.
« 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
The framework assesses an organisation’s maturity across two complementary areas. The first is openness: which data is published, under what licence, in what formats, with what metadata, and at what update frequency. The second is internal and external transparency: the ability to explain where data comes from, how it is produced, who uses it and for what purposes. The FAIR principles (findable, accessible, interoperable, reusable) provide the technical reading grid, whilst governance provides the organisational one.
In practice, this subject is hard to manage because it runs across the whole organisation. Data belongs to the business units, publication often falls to communications or IT, and the opening doctrine, when it exists, remains a statement of principle. The concrete questions are simple to ask and rarely documented: who decides that a dataset becomes open, and on what criteria? Who ensures that a dataset published two years ago is still up to date? Does anyone know who reuses this data, and for what purpose?
One confusion keeps recurring: opening data is not the same as publishing it. A file uploaded without a schema, a data dictionary, a point of contact or a freshness commitment creates no usage at all. Conversely, transparency is not reducible to openness: an organisation can be highly transparent about its processing and models without releasing a single raw dataset. The subject has also changed scale with the European structuring of data sharing and the rise of artificial intelligence uses, which make source traceability and dataset documentation far more demanding than before.
A binary stocktake tells you nothing useful here. Noting that a portal exists or that a charter has been signed says nothing about the actual ability to produce, document, maintain and sustain open data. The maturity assessment places each theme on a progressive scale, inspired by the CMMI method, and pairs each level with the action that moves it to the next. The result is not a verdict, it is a trajectory: a score per theme, a gap to target, and an action plan ordered by effort and impact.
In Datamensio, the framework is ready to use and remains yours. You adjust the themes, questions and levels to your sector, your public or private status and your data scope. AI can rephrase levels, suggest additional questions or build a variant based on your opening doctrine, your data catalogue and your internal documents. The models used can be selected, including from European solutions.
Reference standard: Open data and data transparency maturity framework (FAIR principles, European Open Data Directive)
The themes assessed
Strategy and opening doctrine
Existence of a formalised strategy, criteria for opening decisions, trade-off between openness and restriction, alignment with organisational objectives, senior sponsorship.
Governance and responsibilities
Data owner and steward roles, decision-making bodies, coordination between business, IT and communications, dedicated resources.
Inventory and catalogue
Dataset stocktaking, classification by sensitivity, prioritisation of candidates for opening, catalogue upkeep.
Quality and documentation
Data schemas, variable dictionaries, completeness, pre-publication checks, disclosure of usage limits and series breaks.
Metadata and interoperability
Metadata standards, open machine-readable formats, persistent identifiers, alignment with FAIR principles, shared frameworks.
Distribution, licensing and access
Choice of reuse licences, distribution channels, availability via programmatic interface, access conditions for restricted-distribution data.
Protection and anonymisation
Handling of personal data and secrets, anonymisation and pseudonymisation techniques, re-identification risk assessment, validation before opening.
Transparency on processing
Traceability of data origin, documentation of transformations, disclosure of internal uses, including feeding algorithmic models.
Usage and reuser ecosystem
Knowledge of reusers, point of contact, feedback capture, community engagement, showcasing of reuses.
Measurement and continuous improvement
Freshness, completeness and usage indicators, periodic reviews of the open data portfolio, comparison over time and across entities.
A short version of the framework is available for the online self-assessment.
Frequently asked questions
Does this assessment lead to certification?
No, and it does not replace one. Open data and data transparency are not overseen by a certifying body. The framework measures a maturity level and produces a progression roadmap, not a label.
How is this different from a stocktake of our publications?
A stocktake counts the datasets present on a portal. The assessment evaluates the ability to decide, document, protect, maintain and drive reuse of that data. It places each theme on a scale and shows the action that moves it up a level.
How long does the assessment take?
The short version can be completed in one working session. The full version, run collaboratively with several contributors, typically takes one to two weeks, most of the time being spent gathering input from data owners.
Can the framework be adapted to our context?
Yes. Themes, questions and level wording can all be edited, and you can add your own dimensions. AI can also build a variant based on your opening doctrine and your data catalogue.
How do you reconcile data openness with personal data protection?
The framework addresses both within the same assessment. One theme focuses specifically on anonymisation, re-identification risk assessment and validation before publication. Openness stops where protection requires it, and the assessment measures how solid that boundary is.
Does this also apply to private organisations?
Yes. Sharing data with partners, documenting the sources feeding algorithmic models and being transparent about internal uses raise the same issues, with no obligation to publish to the general public.
Can several entities be compared with each other?
Yes. The same framework can be deployed across several business units, with a benchmark of scores by theme and a comparison with previous assessments. A cross-entity roadmap then consolidates the action plans.
Where is the data hosted?
In France, with OVH, backed up at Scaleway. No transfer outside the European Union. The AI models used can be selected, including from European solutions.