# Data Management Maturity Assessment: [Organisation name]

> Assessment of data management capability across twelve knowledge areas, scored on an original six-level scale (0 to 5) in four dimensions: people and roles, process and policy, technology and tooling, and measurement and control. It covers scope and method, evidence rules, the scale, per-area scoring with evidence, a weighted summary, priority gaps, a roadmap and the reassessment cycle. The knowledge area names follow DAMA-DMBOK (2nd Edition Revised, 2024), cited by name only; no DMBOK or DCAM content is used. Replace every `[bracketed]` value and every row marked *Example*. Worked examples use Corvid Commerce, a fictional SaaS retailer.

<!--
Guidance: How to use this template
- A maturity score is a conversation starter, not a grade. Use it to agree where to invest next, not to rank teams.
- Score from evidence, not opinion. Every level above 1 needs an artefact you can point to (a policy, a role description, a dashboard, an audit result). Section 3 sets the evidence rules.
- Assess the organisation you have, at the scope you chose. A company with one data team and three domains should not aim for level 5 anywhere; most organisations need level 3 in most areas and level 4 only where risk or value is highest.
- Keep the same scale and questions between assessments, or the trend is meaningless. Change the method only in a new major version, and rescore the baseline.
- This template uses its own scale and questions. If your organisation must report against a licensed model (for example a commercial maturity framework or a certification scheme), use that model's own materials under its licence and map the results here.
- Related documents: Data Governance Operating Model (`data/data-governance-operating-model`, its section 10 holds a short capability view that this assessment can replace), Data Catalogue Metadata Standard (`data/data-catalog-metadata-standard`), Data Quality Standard (`data/data-quality-standard`), Data Classification Policy (`data/data-classification-policy`), Business Glossary (`data/business-glossary`), Data Lineage Specification (`data/data-lineage-specification`), Master Data Management Design (`architecture/master-data-management-design`), Data Platform Architecture (`architecture/data-platform-architecture`).
-->

## Document control

<!-- Guidance: Name the sponsor who will act on the results and the assessor who ran the method. Record the period the evidence covers, because maturity changes over time. -->

| Field | Value |
|---|---|
| Document ID | [DMMA-2026-01] |
| Version | [0.1] |
| Status | [Draft / In review / Approved / Superseded] |
| Owner | [@chief-data-officer] |
| Executive sponsor | [@executive-sponsor] |
| Lead assessor | [@assessor] (internal or external) |
| Approvers/reviewers | [@governance-council-chair], [@platform-lead], [@ciso], [@domain-data-owners] |
| Scope | [Legal entities, domains and data estates assessed] |
| Evidence period | YYYY-MM-DD to YYYY-MM-DD |
| Assessment method version | [1.0] (this template) |
| Previous assessment | [Document ID and date, or "baseline"] |
| Next assessment | YYYY-MM-DD |
| Last updated | YYYY-MM-DD |
| Classification | [Internal] |

### Approvals

| Role | Name | Decision | Date |
|---|---|---|---|
| Executive sponsor | [@name] | [Accept results and roadmap / Accept with changes / Reject] | YYYY-MM-DD |
| Chief data officer | [@name] | [Decision] | YYYY-MM-DD |
| Governance council chair | [@name] | [Decision] | YYYY-MM-DD |

### Revision history

| Version | Date | Author | Change summary |
|---|---|---|---|
| 0.1 | YYYY-MM-DD | [@name] | First draft with interview notes |
| 1.0 | YYYY-MM-DD | [@name] | *Example:* Scores calibrated in workshop; roadmap approved |
| [x.y] | YYYY-MM-DD | [@name] | [Change summary] |

## Contents

- [1. Purpose, scope and context](#1-purpose-scope-and-context)
- [2. Maturity scale](#2-maturity-scale)
- [3. Method and evidence rules](#3-method-and-evidence-rules)
- [4. Knowledge areas and weights](#4-knowledge-areas-and-weights)
- [5. Assessment by knowledge area](#5-assessment-by-knowledge-area)
- [6. Results summary](#6-results-summary)
- [7. Priority gaps](#7-priority-gaps)
- [8. Roadmap](#8-roadmap)
- [9. Risks, dependencies and assumptions](#9-risks-dependencies-and-assumptions)
- [10. Reassessment and tracking](#10-reassessment-and-tracking)
- [11. Glossary and references](#11-glossary-and-references)
- [Standard and licence note](#standard-and-licence-note)

## 1. Purpose, scope and context

<!-- Guidance: Say why the assessment is happening now and which decisions it will inform. Questions: is this a baseline, a progress check, or input to a funding decision? Which business goals depend on data? Pitfall: assessing "data management" in the abstract, without the business outcomes that set the targets. -->

[Two to four sentences on the trigger and the decisions the results will inform.]

*Example:* Corvid Commerce is preparing a two-year data programme budget. The board asked which data capabilities limit the growth plan (new markets, a marketplace for third-party sellers) and which carry the most regulatory risk. This baseline assessment scores twelve knowledge areas, sets targets tied to those goals, and ranks the gaps that the programme should fund first.

| Item | In scope | Out of scope |
|---|---|---|
| Organisation | *Example:* All six product domains and the central data platform team | *Example:* Acquired subsidiary (separate assessment after migration) |
| Data | *Example:* Customer, order, product, payment and marketing data | *Example:* HR records (covered by the HR systems review) |
| Business goals informing targets | *Example:* Marketplace launch, entry to two EU markets, audit readiness for finance reporting | [Goal] |

## 2. Maturity scale

<!-- Guidance: Use one scale for every area and dimension. Describe each level in observable terms. This scale is original to this template; keep it or replace it with your organisation's own, but do not mix scales. -->

| Level | Name | Observable description |
|---|---|---|
| 0 | Absent | No recognised practice; nobody is responsible |
| 1 | Ad hoc | Done by individuals when needed; results depend on who does it; nothing written down |
| 2 | Emerging | Practised in some teams; partly documented; owners named in some places; little consistency between teams |
| 3 | Established | A documented standard is used across the scope; roles are named; tools support the practice; exceptions are recorded |
| 4 | Measured | Performance is measured against targets; most checks are automated; results are reviewed and acted on |
| 5 | Embedded | The practice is part of how work is done; improvement is continuous and driven by metrics and feedback; external review confirms it |

### 2.1 Dimensions

Each knowledge area is scored in four dimensions. The area score is the mean of the four dimension scores.

| ID | Dimension | Question it answers |
|---|---|---|
| D1 | People and roles | Are accountable owners and skilled people in place, with time allocated? |
| D2 | Process and policy | Is there a written, agreed and followed way of working? |
| D3 | Technology and tooling | Do tools support and enforce the practice at the scale needed? |
| D4 | Measurement and control | Is the practice measured, and do the measures drive action? |

## 3. Method and evidence rules

<!-- Guidance: Describe how scores were produced so that the next assessment can repeat it. Questions: who was interviewed, which artefacts were reviewed, how were disagreements resolved? Pitfall: self-assessment surveys without evidence, which overstate maturity by a level or more. -->

### 3.1 Steps

```mermaid
flowchart LR
  A[Plan scope and interviewees] --> B[Collect evidence]
  B --> C[Interviews and document review]
  C --> D[Draft scores per dimension]
  D --> E[Calibration workshop]
  E --> F[Final scores and targets]
  F --> G[Gaps and roadmap]
  G --> H[Sponsor approval]
```

| Step | Who | Output |
|---|---|---|
| Plan | Lead assessor, sponsor | Scope, interview list, evidence request |
| Collect evidence | Domain leads | Artefacts in a shared folder, indexed by knowledge area |
| Interviews | Lead assessor with one domain and one platform representative | Notes per area, 45 to 60 minutes each |
| Draft scores | Lead assessor | Score per dimension with evidence reference |
| Calibration | Assessor, domain leads, platform lead, governance lead | Agreed scores; disagreements recorded |
| Targets | Sponsor, CDO | Target per area, linked to business goals |

### 3.2 Evidence rules

| Rule | Detail |
|---|---|
| ER-1 | Level 2 needs at least one artefact showing the practice in at least one team |
| ER-2 | Level 3 needs a documented standard in effect plus evidence of use in most teams in scope |
| ER-3 | Level 4 needs a metric with a target, at least two reporting periods of results, and a recorded action taken on them |
| ER-4 | Level 5 needs level 4 evidence for at least four quarters plus an independent review or audit |
| ER-5 | When evidence is missing, score the lower level and record the missing artefact as a gap |
| ER-6 | Half levels are not used for dimensions; area means may be fractional |

## 4. Knowledge areas and weights

<!-- Guidance: List the areas you assess and set a weight (1 to 3) for each from the business goals in section 1. Weights affect the overall score and the gap ranking, not the area scores. Pitfall: equal weights everywhere, which hides what matters to the business. -->

The knowledge area names below follow the names used by DAMA-DMBOK (2nd Edition Revised); the descriptions and questions are original. DMBOK also covers Big Data and Data Science, which this example leaves out of scope; add it as KA-13 if analytics and machine learning platforms are in scope.

| ID | Knowledge area | What is assessed here | Weight *(Example)* | Reason for weight |
|---|---|---|---|---|
| KA-01 | Data Handling Ethics | Principles for fair and transparent use of data beyond legal minimums | 1 | No high-risk uses planned |
| KA-02 | Data Governance and Stewardship | Decision rights, ownership, stewardship, policies and their enforcement | 3 | Marketplace needs clear ownership of seller data |
| KA-03 | Data Architecture | Target data landscape, domain boundaries, integration patterns | 2 | New markets add systems |
| KA-04 | Data Modeling and Design | Conceptual, logical and physical models; modelling standards | 1 | Practice already reasonable |
| KA-05 | Data Storage and Operations | Running data stores: availability, backup, capacity, cost | 1 | Platform is stable |
| KA-06 | Data Security | Classification, access control, masking, monitoring | 3 | Payment and customer data, two new jurisdictions |
| KA-07 | Data Integration and Interoperability | Pipelines, events, APIs, contracts between producers and consumers | 2 | Seller onboarding adds feeds |
| KA-08 | Document and Content Management | Unstructured content: documents, images, records management | 1 | Low business dependency |
| KA-09 | Reference and Master Data | Shared code lists and master entities such as customer, product, seller | 2 | Seller and product master needed for marketplace |
| KA-10 | Data Warehousing and Business Intelligence | Analytical stores, semantic layer, reporting | 2 | Finance reporting audit |
| KA-11 | Metadata Management | Catalogue, glossary, lineage | 2 | Findability and impact analysis |
| KA-12 | Data Quality Management | Quality dimensions, rules, monitoring, issue resolution | 3 | Finance and marketplace payouts depend on it |

Total weight: 23.

## 5. Assessment by knowledge area

<!-- Guidance: Score each dimension 0 to 5 with evidence. Keep one row per area here and put detailed notes in an appendix or the evidence folder. Questions per area are a starting point; adapt them, then keep them stable between assessments. -->

### 5.1 Scores

| ID | Knowledge area | D1 People | D2 Process | D3 Technology | D4 Measurement | Area score (mean) | Target | Gap |
|---|---|---|---|---|---|---|---|---|
| KA-01 *(Example)* | Data Handling Ethics | 2 | 1 | 1 | 1 | 1.25 | 3 | 1.75 |
| KA-02 *(Example)* | Data Governance and Stewardship | 2 | 2 | 2 | 1 | 1.75 | 3 | 1.25 |
| KA-03 *(Example)* | Data Architecture | 3 | 2 | 3 | 2 | 2.50 | 3 | 0.50 |
| KA-04 *(Example)* | Data Modeling and Design | 3 | 3 | 3 | 2 | 2.75 | 3 | 0.25 |
| KA-05 *(Example)* | Data Storage and Operations | 3 | 3 | 4 | 3 | 3.25 | 4 | 0.75 |
| KA-06 *(Example)* | Data Security | 3 | 3 | 3 | 3 | 3.00 | 4 | 1.00 |
| KA-07 *(Example)* | Data Integration and Interoperability | 3 | 2 | 3 | 2 | 2.50 | 3 | 0.50 |
| KA-08 *(Example)* | Document and Content Management | 1 | 1 | 2 | 1 | 1.25 | 2 | 0.75 |
| KA-09 *(Example)* | Reference and Master Data | 2 | 1 | 2 | 1 | 1.50 | 3 | 1.50 |
| KA-10 *(Example)* | Data Warehousing and Business Intelligence | 3 | 3 | 3 | 2 | 2.75 | 4 | 1.25 |
| KA-11 *(Example)* | Metadata Management | 2 | 2 | 2 | 1 | 1.75 | 3 | 1.25 |
| KA-12 *(Example)* | Data Quality Management | 2 | 2 | 2 | 1 | 1.75 | 3 | 1.25 |

### 5.2 Evidence and observations

| ID | Strengths (with evidence) | Weaknesses (with missing evidence) |
|---|---|---|
| KA-02 *(Example)* | Governance council chartered (charter v1.0); owners named for 40% of shared datasets (domain register export) | No stewardship time allocated; policies not enforced automatically; no governance metrics reported |
| KA-06 *(Example)* | Classification policy approved; role-based access in the warehouse; quarterly access reviews (last two review reports) | Masking applied manually; no tag-based policies; classification coverage not measured |
| KA-09 *(Example)* | Product master in the commerce platform | No seller master; three customer identifiers in use; no match rules documented |
| KA-12 *(Example)* | dbt tests in two domains; incident postmortems for data issues | No shared quality dimensions or thresholds; no quality dashboard; issues tracked in team chats |
| [KA-nn] | [Strengths] | [Weaknesses] |

### 5.3 Assessment questions per area

Use these questions in interviews. Score each dimension from the answers and evidence.

| ID | D1 People | D2 Process | D3 Technology | D4 Measurement |
|---|---|---|---|---|
| KA-01 | Who decides whether a data use is acceptable beyond legal compliance? | Is there a written review step for new or sensitive uses? | Are reviews recorded and searchable? | How many uses were reviewed, changed or stopped? |
| KA-02 | Are owners and stewards named, with time allocated? | Are decision rights and escalation written and followed? | Are policies enforced in the platform? | Are governance metrics reported to the council? |
| KA-03 | Is there an accountable data architect? | Is there an agreed target architecture and review process? | Are architecture decisions recorded and linked to systems? | Is conformance to the target measured? |
| KA-04 | Do modellers have agreed skills and reviewers? | Are modelling standards written and used? | Are models versioned and published? | Are model review findings tracked? |
| KA-05 | Is on-call for data stores staffed? | Are backup, restore and capacity procedures written? | Are restores tested automatically? | Are availability and cost tracked against targets? |
| KA-06 | Is security ownership for data clear? | Are classification and access procedures followed? | Are masking and access policies driven by tags? | Are coverage and exceptions measured? |
| KA-07 | Do producers own their interfaces? | Are contracts agreed before integration? | Are contracts checked in CI? | Are breaking changes and incidents counted? |
| KA-08 | Is someone responsible for content and records? | Are retention and filing rules written? | Does a system enforce them? | Is compliance with the rules sampled? |
| KA-09 | Are master data owners named per entity? | Are match, merge and survivorship rules written? | Is there a system of record or hub? | Are duplicate and match rates measured? |
| KA-10 | Is there a team that owns the analytical platform and semantic layer? | Are metric definitions and release steps written? | Is there a governed semantic layer? | Are usage, freshness and certified share measured? |
| KA-11 | Are catalogue roles filled? | Is there a metadata standard? | Is metadata harvested and lineage captured? | Are completeness and use measured? |
| KA-12 | Are data quality owners named? | Are dimensions, rules and thresholds agreed? | Are checks automated and alerting? | Are quality results reported and acted on? |

## 6. Results summary

<!-- Guidance: Present the overall picture in one table and one chart. Show the calculation so readers trust the number. -->

Overall weighted score = sum of (area score x weight) divided by total weight.

*Example:* with the scores in section 5.1 and the weights in section 4, the weighted current score is 50 / 23 = 2.17 and the weighted target is 74 / 23 = 3.22. The unweighted mean of the area scores is 26 / 12 = 2.17.

| Measure *(Example)* | Value |
|---|---|
| Areas at or above target | 0 of 12 |
| Areas at level 3 or above | 2 of 12 (KA-05, KA-06) |
| Weighted current score | 2.17 |
| Weighted target score | 3.22 |
| Lowest dimension across areas | D4 Measurement (mean 1.67) |

```mermaid
xychart-beta
  title "Current and target score by knowledge area (Example)"
  x-axis ["KA-01", "KA-02", "KA-03", "KA-04", "KA-05", "KA-06", "KA-07", "KA-08", "KA-09", "KA-10", "KA-11", "KA-12"]
  y-axis "Score" 0 --> 5
  bar [1.25, 1.75, 2.5, 2.75, 3.25, 3, 2.5, 1.25, 1.5, 2.75, 1.75, 1.75]
  line [3, 3, 3, 3, 4, 4, 3, 2, 3, 4, 3, 3]
```

Bars show the current score and the line shows the target.

## 7. Priority gaps

<!-- Guidance: Rank gaps by gap size times weight, then adjust for dependencies and quick wins. Write each gap so that it can become a funded piece of work. -->

Priority index = (target minus current) x weight.

| Rank | ID | Knowledge area | Gap | Weight | Priority index | Main missing capability | Depends on |
|---|---|---|---|---|---|---|---|
| 1 *(Example)* | KA-02 | Data Governance and Stewardship | 1.25 | 3 | 3.75 | Owners for all shared data; stewardship time; governance metrics | None |
| 1 *(Example)* | KA-12 | Data Quality Management | 1.25 | 3 | 3.75 | Shared quality standard, automated checks, quality dashboard | KA-02 |
| 3 *(Example)* | KA-06 | Data Security | 1.00 | 3 | 3.00 | Tag-based masking and access, classification coverage metric | KA-11 |
| 3 *(Example)* | KA-09 | Reference and Master Data | 1.50 | 2 | 3.00 | Seller master and customer identity resolution | KA-02 |
| 5 *(Example)* | KA-10 | Data Warehousing and Business Intelligence | 1.25 | 2 | 2.50 | Certified semantic layer for finance metrics | KA-12 |
| 5 *(Example)* | KA-11 | Metadata Management | 1.25 | 2 | 2.50 | Metadata standard, lineage, glossary | KA-02 |
| 7 *(Example)* | KA-01 | Data Handling Ethics | 1.75 | 1 | 1.75 | Review step for sensitive uses | KA-02 |
| [n] | [KA-nn] | [Area] | [Gap] | [Weight] | [Index] | [Capability] | [KA-nn] |

Ties share a rank. Areas with an index below 1.5 are not funded in this cycle unless they block a higher-ranked gap.

## 8. Roadmap

<!-- Guidance: Group gap closure into phases with exit criteria that can be checked with the evidence rules in section 3. The diagram must show the same dependencies as the table in section 7. -->

| Phase | Window | Areas | Deliverables | Exit criteria (evidence) |
|---|---|---|---|---|
| Foundation *(Example)* | Quarters 1 and 2 | KA-02, KA-11 | Owners for all shared datasets; stewardship time agreed; metadata standard and catalogue live; business glossary for top 50 terms | KA-02 D1 and D2 at 3; KA-11 D2 and D3 at 3 |
| Control *(Example)* | Quarters 2 and 3 | KA-12, KA-06 | Data quality standard and checks in CI; tag-driven masking; classification coverage reported | KA-12 at 3; KA-06 D3 at 4 |
| Master and BI *(Example)* | Quarters 3 and 4 | KA-09, KA-10 | Seller master; customer identity rules; certified finance metrics | KA-09 at 3; KA-10 D3 at 4 |
| Measure *(Example)* | Quarters 5 to 8 | All funded areas | Metrics with targets for each funded area; reassessment | D4 at 3 or above in funded areas |

```mermaid
flowchart LR
  G[KA-02 Governance] --> Q[KA-12 Data quality]
  G --> M[KA-09 Master data]
  G --> MD[KA-11 Metadata]
  G --> E[KA-01 Ethics]
  MD --> S[KA-06 Security]
  Q --> B[KA-10 Warehousing and BI]
```

## 9. Risks, dependencies and assumptions

<!-- Guidance: Record what could make the scores misleading or the roadmap fail. -->

| ID | Type | Description | Mitigation | Owner |
|---|---|---|---|---|
| MRA-001 *(Example)* | Risk | Scores overstated because interviewees were team leads only | Add practitioner interviews in the next cycle | Lead assessor |
| MRA-002 *(Example)* | Dependency | Roadmap needs two steward roles funded | Sponsor decision in budget round | CDO |
| MRA-003 *(Example)* | Assumption | Marketplace launch date stays in quarter 4 | Re-weight KA-09 if the date moves | Sponsor |
| [MRA-nnn] | [Type] | [Description] | [Mitigation] | [@owner] |

## 10. Reassessment and tracking

<!-- Guidance: Decide when to reassess and what is tracked between assessments. A full reassessment every 12 months, with a light check on funded areas each quarter, suits most organisations. -->

| Activity | Frequency | Scope | Output |
|---|---|---|---|
| Light check | Quarterly | Funded areas only, D4 and exit criteria | Status in governance council pack |
| Full reassessment | Every 12 months | All areas, same questions and scale | New version of this document |
| Method review | Every 24 months | Scale, dimensions, questions | Method version change, baseline rescored if changed |

| Area | Baseline YYYY-MM | Reassessment YYYY-MM | Change |
|---|---|---|---|
| KA-02 *(Example)* | 1.75 | [Score] | [Change] |
| [KA-nn] | [Score] | [Score] | [Change] |

## 11. Glossary and references

<!-- Guidance: Define the words used in scoring and list the sources, with the date you checked them. -->

### 11.1 Glossary

| Term | Meaning in this document |
|---|---|
| Knowledge area | A group of related data management activities assessed together |
| Dimension | One of four views (people, process, technology, measurement) used to score an area |
| Area score | Mean of the four dimension scores for an area |
| Weight | Business importance of an area in this cycle, 1 to 3 |
| Priority index | Gap multiplied by weight, used to rank gaps |
| Calibration | A workshop in which assessors and area leads agree final scores from evidence |

### 11.2 References

| Reference | Version or date checked | Use |
|---|---|---|
| DAMA International, DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition Revised, https://dama.org/learning-resources/dama-data-management-body-of-knowledge-dmbok/ | 2nd Edition Revised (2024); checked 2026-10-30 | Knowledge area names only |
| DAMA International, DMBOK2R infographics page, https://dama.org/dmbok2r-infographics/ | Checked 2026-10-30 | Confirmation of knowledge area names |
| [Previous assessment] | [Date] | Baseline |

## Standard and licence note

Structure aligned with the knowledge area names used in DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition Revised (DAMA International, 2024). DAMA-DMBOK is a copyrighted publication and is cited by name only; no text, figure, wheel diagram, context diagram, definition or maturity content from it is reproduced, and no content from the EDM Council DCAM or any other licensed maturity model is used. Official text: DAMA International, purchase or licensed access: https://dama.org/learning-resources/dama-data-management-body-of-knowledge-dmbok/. The maturity scale, dimensions, evidence rules, questions, scoring formulas and examples are original, and Corvid Commerce is fictional. This template is original text written for this collection. Check the current edition of the works cited before use. Template licence: MIT.