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Model 29 of 74 · Technology & Data

Data Management & Analytics Maturity Assessment

Evaluate how well data is governed, managed, and used for decision-making.

Pillars & areas

Govern

Control the foundations

  1. 01Data Governance & Ownership
  2. 02Data Quality & Standards

Architect

Build the platform

  1. 03Data Architecture & Integration
  2. 06Data Strategy & Value

Analyse

Unlock the value

  1. 04Analytics & Insight
  2. 05Data Literacy & Culture

Levels

  1. 1SiloedSiloed & intuition-based
  2. 2GovernedGoverned & measured
  3. 3Data-DrivenStrategic & anticipatory

01 Data Governance & Ownership

Are roles, policies, and accountability structures in place to ensure data is managed as an organisational asset?

0/4 answered

DG-1How does your organisation assign accountability for data?

Think about whether specific people are responsible for the accuracy, completeness, and appropriate use of key data sets.

DG-2How are data policies and standards defined and enforced?

Consider whether there are written rules about how data should be created, stored, shared, and retired — and whether anyone checks compliance.

DG-3How does your organisation manage data access and security?

Think about whether access to sensitive data is controlled by policy and role, or whether people can access whatever they can find.

DG-4How does your organisation handle regulatory and compliance requirements related to data?

Consider whether privacy, retention, and sovereignty obligations are systematically tracked and met, or handled case-by-case.

02 Data Quality & Standards

Does the organisation actively measure, monitor, and improve the quality of its data?

0/4 answered

DQ-1How does your organisation define what 'good quality data' means?

Think about whether there are agreed dimensions of quality (accuracy, completeness, timeliness, consistency) with measurable thresholds.

DQ-2How are data quality issues detected and resolved?

Consider whether problems are caught at the point of entry, during processing, or only when a downstream consumer complains.

DQ-3How does your organisation manage master and reference data?

Think about whether core entities like customers, products, or locations have a single authoritative source, or whether every system maintains its own version.

DQ-4How does your organisation track and report on data quality over time?

Consider whether there are dashboards or scorecards showing quality trends, or whether quality is only discussed anecdotally.

03 Data Architecture & Integration

Is data structured, stored, and integrated in a way that makes it accessible, consistent, and fit for purpose?

0/4 answered

DA-1How well documented is your organisation's data landscape?

Think about whether someone new to the organisation could find out what data exists, where it lives, and what it means without asking around.

DA-2How does your organisation move data between systems?

Consider whether integrations are built ad hoc with point-to-point connections, or whether there is a deliberate integration architecture.

DA-3How does your organisation manage its analytical data stores?

Think about whether reporting and analytics draw from a curated, trusted data store or directly from operational systems.

DA-4How does your organisation plan and evolve its data architecture?

Consider whether data architecture decisions are made strategically or whether the current state is simply the accumulation of individual project choices.

04 Analytics & Insight

Does the organisation use data effectively to generate insights that inform decisions and drive action?

0/4 answered

AI-1How does your organisation use data to inform decisions?

Think about whether decisions at different levels of the organisation are backed by data, or whether they rely primarily on experience and gut feeling.

AI-2What types of analytics does your organisation routinely perform?

Consider whether the organisation mainly looks backward (what happened) or also looks forward (what might happen, what should we do).

AI-3How accessible are analytics tools and outputs to people across the organisation?

Think about whether insights are locked inside a specialist team or whether business users can explore data themselves.

AI-4How does your organisation measure whether analytics actually drives better outcomes?

Consider whether there is evidence that data-informed decisions produce better results, or whether analytics effort is assumed to be valuable without proof.

05 Data Literacy & Culture

Do people across the organisation have the skills, confidence, and habits to work with data effectively?

0/4 answered

DL-1How confident are people in your organisation at interpreting and using data?

Think about whether the average employee can read a chart, question a data set, or use data to support an argument — not just data specialists.

DL-2How does your organisation invest in building data skills?

Consider whether data training is available, targeted, and actually taken up — or whether it is absent or optional and ignored.

DL-3How does your organisation's culture encourage or discourage the use of data?

Think about whether people who challenge decisions with data are welcomed, or whether data is used selectively to confirm what leaders already believe.

DL-4How does your organisation handle data ethics and responsible use?

Consider whether there are guidelines for ethical data use (e.g. avoiding bias, protecting privacy, being transparent about algorithms) and whether people follow them.

06 Data Strategy & Value

Does the organisation have a clear vision for how data creates value, with a roadmap and investment to deliver it?

0/4 answered

DS-1Does your organisation have a data strategy?

Think about whether there is a documented plan that connects data capabilities to business outcomes, or whether data investment happens opportunistically.

DS-2How does your organisation quantify the value that data delivers?

Consider whether the organisation can point to specific examples where data investment produced measurable business outcomes.

DS-3How well aligned is data investment with business priorities?

Think about whether data teams work on what the business needs most, or whether they set their own agenda based on technical interest.

DS-4How does your organisation explore new ways to create value from data?

Consider whether the organisation actively experiments with data monetisation, new data products, or innovative uses of existing data assets.

Self-assessment report

Data Management & Analytics

Answer the questions above and your report builds here: an overall score, a score and target for each area, and the three changes that matter most.

About this model

About the Data Management & Analytics maturity model

Data is one of the few assets that appreciates with use. Yet most organisations treat data as a by-product of operations rather than a resource to be deliberately governed, cultivated, and leveraged. This maturity model starts from the premise that data only creates value when it is trusted, accessible, and connected to decisions. The assessment does not reward technology for its own sake — it rewards the organisational habits, structures, and skills that turn raw data into reliable insight and measurable outcomes.

The Data Management & Analytics maturity model evaluates six interconnected areas that together determine how effectively an organisation manages and exploits its data assets. It covers the full spectrum from foundational governance (who owns data, what rules apply) through quality management, architecture, and analytics capability, to the strategic and cultural conditions that determine whether data investment translates into business value.

The model uses 24 questions across 6 areas, each scored at three maturity levels. It is designed to surface specific, actionable gaps rather than produce a single abstract score. Organisations can use the results to prioritise investment, benchmark progress, and build a roadmap for data capability improvement.

Who this assessment is for

This assessment is designed for:

  • Chief Data Officers and data leadership teams looking to benchmark current capability and build a prioritised improvement roadmap.
  • IT and technology leaders responsible for data architecture, platforms, and integration who want to understand how well technical infrastructure supports business data needs.
  • Business executives and strategists evaluating whether the organisation's data capabilities are sufficient to support strategic goals.
  • Data governance practitioners and data stewards seeking a structured framework to assess and communicate the state of governance maturity.
  • Analytics and BI managers wanting to understand how their team's work fits into the broader organisational data picture.
  • Consultants and advisors helping clients assess data maturity and develop transformation plans.

The model is framework-agnostic and applicable across industries, organisation sizes, and technology stacks.

The three maturity levels

Level 1 — Siloed

Data is managed informally and inconsistently. There is no clear ownership, no enforced quality standards, and no systematic approach to data governance or architecture. Analytics is limited to backward-looking reports produced by specialists on request. Decisions are driven primarily by experience and intuition. Data issues are discovered only when they cause visible problems, and fixes address symptoms rather than root causes. The organisation treats data as a cost of doing business rather than a source of value.

Level 2 — Governed

Core data assets are identified, owned, and governed through defined processes. Quality rules are documented and enforced for critical data domains. A data warehouse or analytical platform supports regular reporting, and business users have access to standardised dashboards. A data strategy exists and is endorsed by leadership. Data skills training is available for key roles. The organisation consistently uses data to inform major operational and strategic decisions, though pockets of manual process, siloed data, and intuition-driven decision-making persist.

Level 3 — Data-Driven

Data is treated as a strategic asset at the enterprise level. Governance is comprehensive, automated, and continuously enforced. Quality is measured in real time against published targets and is visible to stakeholders. A modern data architecture supports self-service analytics and advanced techniques including predictive and prescriptive modelling. Data literacy is widespread, and the culture actively rewards evidence-based decision-making. The data strategy is tightly coupled with business strategy, value is systematically tracked, and the organisation continuously explores new ways to create value from its data assets.

The 6 areas assessed

1. Data Governance & Ownership

Governance is the foundation of data maturity. This area assesses whether the organisation has assigned clear accountability for data assets, established and enforced data policies and standards, implemented appropriate access controls, and systematically managed regulatory and compliance obligations. Without governance, every downstream capability — quality, architecture, analytics — is built on an unreliable foundation.

2. Data Quality & Standards

Poor data quality is the most common reason analytics initiatives fail to deliver value. This area examines whether the organisation has defined what good quality means, implemented automated detection and resolution of quality issues, managed master and reference data through authoritative sources, and established ongoing measurement and reporting of quality trends. The focus is on systematic quality management rather than heroic data cleaning.

3. Data Architecture & Integration

Architecture determines whether data can flow reliably from where it is created to where it is needed. This area evaluates the documentation and discoverability of the data landscape, the maturity of integration patterns used to move data between systems, the state of analytical data stores, and whether data architecture is planned strategically or simply accumulated through project-by-project decisions.

4. Analytics & Insight

Data only creates value when it informs action. This area assesses how effectively the organisation uses data to support decisions, the sophistication of analytics techniques in regular use (from descriptive to predictive and prescriptive), the accessibility of analytics tools and outputs to non-specialists, and whether analytics impact is measured to confirm that data-informed decisions produce better outcomes.

5. Data Literacy & Culture

Technology and governance frameworks are necessary but insufficient — people must be willing and able to use data effectively. This area examines the general confidence and skill level of non-specialist staff in working with data, the organisation's investment in data skills development, whether the culture genuinely rewards evidence-based decision-making, and the maturity of data ethics and responsible use practices.

6. Data Strategy & Value

Without a strategy, data investment is scattered and its value is unproven. This area evaluates whether the organisation has a coherent data strategy connected to business goals, whether data value is quantified and tracked, how well data investment is aligned with business priorities, and whether the organisation actively explores new ways to create value from data through innovation and experimentation.

How this model relates to other frameworks

  • DAMA DMBOK (Data Management Body of Knowledge) — The most comprehensive reference framework for data management disciplines, covering governance, quality, architecture, metadata, and more.
  • DCAM (Data Management Capability Assessment Model) — EDM Council's structured assessment framework used widely in financial services to benchmark data management maturity.
  • ISO 8000 (Data Quality) — International standard for data quality management, providing a formal framework for measuring and improving data quality.
  • Data Management Maturity (DMM) Model — the CMMI Institute's (now part of ISACA) maturity model for data management practices and capability levels.
  • IBM Data Governance Council Maturity Model — A widely referenced model that assesses data governance maturity across organisational, process, and technology dimensions.
  • Gartner Data and Analytics Maturity Model — An analyst framework that benchmarks data and analytics capability across five levels from basic to transformational.

Frequently asked questions

What is the Data Management & Analytics maturity model?

Evaluate how well data is governed, managed, and used for decision-making. It is an original, simplified model with three maturity levels (Siloed, Governed and Data-Driven), assessed through 24 questions across 6 areas: Data Governance & Ownership, Data Quality & Standards, Data Architecture & Integration, Analytics & Insight, Data Literacy & Culture and Data Strategy & Value.

How long does the Data Management & Analytics assessment take?

Most people finish in 10–14 minutes. Each of the 24 questions has three concrete answer options. Your progress is saved in the page URL, so you can bookmark it and come back later.

How is the score calculated?

Each answer scores 1 (Siloed), 2 (Governed) or 3 (Data-Driven). An area's score is the average of its answered questions, and the overall score is the average of the area scores, so every area counts equally. Scores run from 1.0 to 3.0.

Is my data stored or shared?

No. Your answers are never sent to a server. They are stored only in the page URL, which you control. Copy the link to share results with colleagues, or export a PDF or CSV report.

How should I use the results?

Start with "Where to focus" in your report. It lists the questions with the biggest gap to the target you set for each area, and shows what the next level looks like in practice. For a fuller picture, ask several people from different teams to complete the assessment and compare their links. Differences in perception are often as revealing as the scores themselves.