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Model 38 of 74 · Technology & Data

Data Governance Maturity Assessment

Assess how well your organisation owns, defines, protects, and manages the quality of its data.

Pillars & areas

Own

Decide who's accountable

  1. 01Accountability & Stewardship
  2. 02Policies & Standards

Define

Make it understandable

  1. 03Data Quality Management
  2. 04Metadata, Catalogue & Lineage

Trust

Make it trustworthy

  1. 05Access, Usage & Ethics
  2. 06Data Lifecycle & Governance Value

Levels

  1. 1OwnerlessNobody owns it
  2. 2StewardedOwned & defined
  3. 3Trusted by DesignBuilt-in & trusted

01 Accountability & Stewardship

Is it clear who owns each important set of data and who is responsible for looking after it?

0/4 answered

AS-1How is ownership of data assigned?

Think about whether there is a named business person accountable for each important data domain, such as customer, product, employee or finance data.

AS-2Who looks after data day to day?

Consider whether there are data stewards or similar roles who maintain definitions, resolve quality issues and answer questions.

AS-3How are data governance decisions made?

Think about who decides on definitions, standards, conflicts between teams and access to sensitive data.

AS-4How strongly do senior leaders support data governance?

Consider whether leaders treat data governance as important, fund it and hold people accountable.

02 Policies & Standards

Are there clear, practical rules and agreed definitions for how data is managed?

0/4 answered

PS-1What data policies does your organisation have?

Think about policies covering ownership, quality, classification, access, retention and acceptable use.

PS-2How are business terms defined?

Consider whether terms such as 'active customer', 'revenue' or 'headcount' have one agreed definition.

PS-3How are data standards managed?

Think about standards for formats, codes and reference data such as country codes, product categories and organisational hierarchies.

PS-4How is compliance with data policies checked?

Consider whether anyone checks that policies are actually followed.

03 Data Quality Management

Is data quality defined, measured and improved at its source?

0/4 answered

DQ-1How are data quality requirements defined?

Think about whether anyone has said how complete, accurate, timely and consistent each important dataset needs to be.

DQ-2How is data quality measured and reported?

Consider whether quality is measured regularly and visible to owners and users.

DQ-3How are data quality issues resolved?

Think about whether issues are fixed once at their root cause, or corrected repeatedly downstream.

DQ-4How is poor quality data prevented at the point of entry?

Consider validation in forms and systems, training for people who enter data, and checks when data is received from others.

04 Metadata, Catalogue & Lineage

Can people find the data they need, understand what it means, and see where it came from?

0/4 answered

ML-1How do people find the data they need?

Think about whether there is a catalogue or inventory, or whether people rely on asking around.

ML-2How well is the meaning and context of data documented?

Consider whether someone new could understand a dataset — what each field means, how it is calculated and its limitations.

ML-3How well can your organisation trace where data comes from?

Think about whether you can follow a figure in a report back through its transformations to its source systems.

ML-4Has your organisation identified its most critical data?

Consider whether you know which data elements matter most for operations, reporting, regulation and decisions.

05 Access, Usage & Ethics

Is access to data controlled appropriately, and is data used responsibly and ethically?

0/4 answered

AU-1How is data classified?

Think about whether data is labelled by sensitivity, such as public, internal, confidential and restricted, and whether labels drive controls.

AU-2How is access to data requested and granted?

Consider whether access depends on knowing the right person, or follows a clear and timely process.

AU-3How is data shared with third parties governed?

Think about data shared with suppliers, partners, regulators and customers.

AU-4How are ethical considerations addressed in data use?

Consider whether anyone asks 'should we?' as well as 'can we?' when data is used in new ways, including for analytics and AI.

06 Data Lifecycle & Governance Value

Is data managed from creation to deletion, and does governance demonstrably add value?

0/4 answered

LV-1How is data retention and disposal managed?

Think about whether data is deleted when it is no longer needed, or kept indefinitely.

LV-2How is governance considered when new data initiatives start?

Consider new systems, data products, analytics projects and AI initiatives.

LV-3How well do people across the organisation understand data and their responsibilities?

Think about data literacy and awareness of governance responsibilities beyond the data team.

LV-4How does your organisation measure the value of data governance?

Consider whether governance is judged by its activity (policies written, meetings held) or by outcomes.

Self-assessment report

Data Governance

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 Governance maturity model

Every organisation says data is an asset, but few manage it like one. Nobody can say who owns the customer record, two reports show different revenue figures, sensitive data is copied into spreadsheets nobody controls, and analysts spend more time finding and cleaning data than using it. The cause is rarely technology. It is the absence of clear decisions about who is accountable for data, what it means, how good it needs to be, and who may use it for what.

This model assesses data governance as the system of accountability and decision-making that makes data trustworthy and usable. It deliberately avoids treating governance as bureaucracy: the aim is not more committees and policies, but the minimum structure needed so that people can find data, understand it, trust it and use it responsibly. It complements the broader Data Management & Analytics model by going deeper into ownership, standards, quality, metadata, access and lifecycle.

The Data Governance maturity model contains 24 questions across 6 areas: accountability and stewardship, policies and standards, data quality management, metadata, catalogue and lineage, access, usage and ethics, and data lifecycle and governance value. Each question describes three levels of maturity through observable practices. It is relevant to any organisation that relies on data for operations, reporting, analytics or AI, regardless of size or technology.

Who this assessment is for

  • Chief data officers and heads of data
  • Data owners, data stewards and data governance teams
  • Analytics, BI and data engineering leaders
  • Privacy, risk and compliance professionals
  • Business leaders who depend on accurate reporting and data-driven decisions

The three maturity levels

  • Level 1 — Ownerless: Data has no clear owners, so when it is wrong nobody is responsible for fixing it. Key terms mean different things in different teams, and reports regularly disagree. Quality problems are fixed by hand, over and over, by whoever notices them. Nobody knows what data exists, where it came from or who can see it, and sensitive data spreads into uncontrolled copies.
  • Level 2 — Stewarded: Owners and stewards are assigned for critical data domains, and a governance forum resolves cross-domain issues. Core business terms have agreed definitions, quality rules are defined and measured for critical data, and a catalogue shows what key data exists and where. Access is granted through a defined process based on classification, and retention rules are documented.
  • Level 3 — Trusted by Design: Governance is part of everyday work rather than a separate activity. Ownership is clear for all important data, definitions and lineage are visible where people use data, quality is managed at the source, and access decisions are fast, consistent and auditable. Ethical use is considered for every significant use of data, and the organisation can show that governance improves decisions, reduces risk and saves time.

The 6 areas assessed

1. Accountability & Stewardship

Who is accountable for data and how governance decisions are made. This area covers data ownership, stewardship roles, governance forums and decision rights, and executive sponsorship.

2. Policies & Standards

The rules and shared language for data. This area covers data policies, business glossary and definitions, data standards for formats and reference data, and how compliance with policies is checked.

3. Data Quality Management

How the organisation ensures data is fit for purpose. This area covers quality requirements, measurement and reporting, issue management and root cause resolution, and preventing poor quality at the point of entry.

4. Metadata, Catalogue & Lineage

How data is documented and made discoverable. This area covers the data catalogue, the documentation of meaning and context, data lineage, and the identification of critical data elements.

5. Access, Usage & Ethics

How the organisation balances access and protection. This area covers data classification, access request and approval, data sharing with third parties, and ethical considerations in data use, including for analytics and AI.

6. Data Lifecycle & Governance Value

How data is managed over time and how governance proves its worth. This area covers retention and disposal, governance of new data initiatives, data literacy, and measuring the value of data governance.

How this model relates to other frameworks

This is an original, simplified maturity model and is not derived from or intended to replace any of the following. However, organisations that use this assessment may also find value in these established frameworks:

  • DAMA-DMBOK (Data Management Body of Knowledge) — a widely used reference for data management and governance practices
  • ISO/IEC 38505 (Governance of data) — guidance for governing bodies on the governance of data
  • ISO 8000 (Data quality) — a series of standards on data quality and master data
  • EDM Council DCAM — a data management capability assessment model widely used in financial services
  • FAIR principles — principles for making data findable, accessible, interoperable and reusable

Frequently asked questions

What is the Data Governance maturity model?

Assess how well your organisation owns, defines, protects, and manages the quality of its data. It is an original, simplified model with three maturity levels (Ownerless, Stewarded and Trusted by Design), assessed through 24 questions across 6 areas: Accountability & Stewardship, Policies & Standards, Data Quality Management, Metadata, Catalogue & Lineage, Access, Usage & Ethics and Data Lifecycle & Governance Value.

How long does the Data Governance 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 (Ownerless), 2 (Stewarded) or 3 (Trusted by Design). 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.