Big data readiness and analytics maturity
Your analytics capability 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.
Big data readiness and analytics maturity
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 Datamensio analytics maturity framework, 5-level CMMI scale 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 that connects them) that turns an observation into a trajectory.
Are key indicators shared across entities based on common, documented definitions?
- N1
No shared definitions. Each entity calculates its indicators using its own rules, and discrepancies are discovered in meetings.
- N2
Definitions exist for a few indicators, in scattered documents. Their application depends on the team producing the figure.
- N3
Key indicators are defined in an accessible dictionary, with an identified owner. Reporting outputs refer to it, though some discrepancies remain.
- N4
Definitions are applied systematically, calculations are tooled from a single source and discrepancies are detected by automated controls.
- N5
The dictionary is revised as business needs evolve, every change is versioned, tracked and its impact assessed on existing reporting outputs.
Action to move from L2 to L3
Build a single dictionary of key indicators, appoint a business owner for each indicator and put its validation on the agenda of the quarterly data committee before any new reporting output.
« 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 ability to exploit growing data volumes to decide and act. It covers the technical foundation (ingestion, storage, distributed processing, real time), data quality and documentation, available analytics skills, how use cases emerge and are prioritised, and the actual use of results in decisions. Each theme is placed on a five-level maturity scale built using the CMMI method: from absent practice to practice that is measured and continuously improved.
In practice, analytics maturity is hard to manage because it is spread across an IT department, business functions and sometimes a central data team, with no single owner. The questions leadership teams ask are concrete. How many dashboards are actually consulted, and by whom? Do key indicators share the same definition across business units? Does a new use case move from idea to production, or does it stay a prototype shown once in a committee?
A common confusion recurs: equipment does not create maturity. Many organisations have acquired a data lake, a cloud warehouse and visualisation tools, then find that usage does not follow. What is missing is almost never the technical brick: it is data ownership, shared definitions of indicators, the ability to industrialise a model and the discipline of measuring the value produced. The framework explicitly separates technical capabilities from usage capabilities, so a modern foundation cannot mask weak management.
The maturity assessment does not answer the binary question of a requirements checklist. It places each theme at an observable level and indicates the action that moves it up a level. An overall score without a trajectory helps no one: it is the gap between the observed level and the target level that feeds the action plan, which AI then groups into a prioritised roadmap, with cost, timeframe and expected impact on the score.
In Datamensio, the framework is ready to use and belongs to you. You adjust themes, questions and level wording to suit your sector and organisation. The AI refines these levels, suggests additional questions or builds a variant from your own documents: data master plan, application map, briefing note for a transformation programme.
Reference standard: Datamensio analytics maturity framework, 5-level CMMI scale
The themes assessed
Strategy and use cases
Existence of a formalised data ambition, link to business objectives, process for qualifying and prioritising use cases, measurement of value produced.
Governance and data ownership
Data owner and steward roles, decision-making bodies, management policies, arbitration of definition conflicts between entities.
Technical foundation and architecture
Ingestion, storage and distributed processing capabilities, elasticity, separation of environments, batch and streaming processing management.
Quality and data lifecycle
Quality rules, automated controls, data flow traceability, master data management, archiving and deletion.
Catalogue and documentation
Dataset inventory, indicator dictionary, technical and business metadata, accessibility of the catalogue to users.
Skills and organisation
Available profiles (engineering, analysis, data science), split between central team and business functions, upskilling pathways, dependency on external providers.
Advanced analytics and models
Move from descriptive to predictive, model industrialisation, production performance monitoring, retraining, documentation of choices.
Dissemination and use in decision making
User autonomy, actual usage rate of reporting outputs, integration of indicators into management rituals, tracked decisions.
Security, protection and compliance of usage
Access management, classification and protection of personal or sensitive data, anonymisation, logging, applicable framework for processing.
Costs and platform management
Visibility on consumption costs, chargeback to business units, processing optimisation, service indicators.
A short version of the framework is available for the online self-assessment.
Frequently asked questions
Does this assessment lead to certification?
No, and that is not its purpose. Analytics maturity has no certifying body. The framework measures capabilities and produces a progression trajectory, usable in front of an executive committee or a shareholder.
How is this different from an audit?
An audit checks for the presence of expected elements and concludes with a gap. The maturity assessment places each theme on a progressive scale and indicates the action that raises it a level. The result is not a finding but an action plan.
How long does the assessment take?
The short version takes 20 to 30 minutes to complete by a lead who knows the setup. The full version, run collaboratively with contributors from IT and business functions, spans one to two weeks, with most of the time spent on data collection.
Can the framework be adapted to our sector?
Yes. You modify the questions, the wording of levels, add your own themes or start from a blank slate. The AI suggests adjustments and can build a variant from your internal documents, for example your data master plan.
How can several business units be compared?
The assessment can be rolled out industrially across several entities using the same framework. The benchmark compares scores by theme across entities and against previous campaigns. A cross-cutting roadmap consolidates action plans to avoid funding the same building block twice.
Do respondents need technical expertise?
The questions focus on practices and organisation, not configuration settings. Some require input from an architect or data engineer: the collaborative mode allows these questions to be assigned to the right person.
How does this relate to an artificial intelligence project?
Data readiness underpins most AI use cases. This assessment identifies the blocking themes, quality, catalogue, industrialisation, before launching an AI programme. It cross-references with the AI maturity and data governance frameworks.
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.