Data Management Maturity Assessment Template

Original 0 to 5 maturity scale, four dimensions, evidence rules, twelve knowledge areas with weights, scoring, priority gaps, roadmap, reassessment

Data Engineering & GovernanceFullDAMA-DMBOK 2nd Edition Revised (knowledge area names only)management maturity assessmentUpdated Oct 29, 2026

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How to use: Use it when you need a baseline or progress check of data management capability to decide where to invest. Fill the scope, the business goals and the weights first, then score only from evidence. The main pitfall is self-assessment without evidence, which overstates maturity and hides the real gaps.

Preview

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.

Document control

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Status	[Draft / In review / Approved / Superseded]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]

1. Purpose, scope and context
2. Maturity scale
3. Method and evidence rules
4. Knowledge areas and weights
5. Assessment by knowledge area
6. Results summary
7. Priority gaps
8. Roadmap
9. Risks, dependencies and assumptions
10. Reassessment and tracking
11. Glossary and references
Standard and licence note

1. Purpose, scope and context

[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

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

3.1 Steps

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

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

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

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)

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

7. Priority gaps

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

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

9. Risks, dependencies and assumptions

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

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

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.

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