Common language
Business, IT and the data team talk about the same thing when they say "data quality", "stewardship" or "lineage". Discussions are about substance, not terminology.
DAMA DMBoK explained
The DAMA DMBoK framework gives data management a shared language and method — 11 knowledge areas, one coherent whole. This page explains what it is, how it works and why more and more Dutch organisations are adopting it.
DAMA DMBoK stands for Data Management Body of Knowledge, published by DAMA International — the global professional association for data management practitioners (founded 1980). The framework describes data management as 11 interconnected knowledge areas, with Data Governance at the centre. DMBoK v2 (2017) is the current standard; v3 (anticipated) brings explicit additions for AI governance and cloud-native architecture. Worldwide it is the de facto reference for data management methodology, used by governments, financial institutions and large enterprises. In the Netherlands DAMA NL facilitates the local community, knowledge sharing and certification (CDMP).
Why now
Many organisations work on data without a shared framework. That scales poorly. Here is what DMBoK adds where pragmatism alone falls short.
Business, IT and the data team talk about the same thing when they say "data quality", "stewardship" or "lineage". Discussions are about substance, not terminology.
The 11 knowledge areas together cover the entire data practice. No blind spots like "we forgot to consider security".
Per knowledge area a maturity level can be determined (CMMI style, 1 to 5). That makes progress concrete and comparable.
EU AI Act, GDPR and Data Act impose requirements that land directly in DMBoK knowledge areas: Data Governance, Metadata, Data Security and Data Quality.
The framework is methodological, not technological. Works on Qlik, Snowflake, erwin — and everything you already have.
When we're gone, your own team knows where the approach came from. It isn't a "cimt method" — it's an open, published framework.
The 11 knowledge areas
For each knowledge area: what it is, which questions it answers, and how cimt brings it to practice.
The centre of the DAMA wheel. Ownership, policy, stewardship and decision-making on data. Without governance the other knowledge areas stall in good intentions.
Typical questions
Structural blueprint of data flows, storage and integration. How data travels from source to destination, which platforms play which role, and how it scales.
Typical questions
Conceptual, logical and physical models that give data shape. Canonical models and master data structures consistent across all systems.
Typical questions
Management of databases, data lakes and lakehouses in production. Performance, availability, backup and monitoring — the operational side of data management.
Typical questions
Access control, encryption, classification and privacy by design. Inseparable from governance and compliance (GDPR, EU AI Act, Data Act).
Typical questions
ETL, ELT, CDC, real-time streaming, API integration — all data movements between systems. The most technology-intensive knowledge area.
Typical questions
Unstructured data: contracts, emails, documents. Increasingly important with GenAI and RAG applications that lean on this data.
Typical questions
Golden record for customer, product, supplier and location. One truth recognised by all systems, with canonical models as foundation.
Typical questions
Data platforms for analytics, reporting and self-service BI. Lakehouse architecture on Snowflake; analytics layer in Qlik Sense.
Typical questions
Data about data: catalog, lineage, business glossary. Essential for traceability, AI Act compliance and the business ↔ IT bridge.
Typical questions
Completeness, consistency, timeliness and reliability. The factor that determines whether AI models, BI dashboards and compliance reports deliver value — or not.
Typical questions
DAMA NL & certification
DAMA NL is the Dutch chapter of DAMA International. It organises events, knowledge sessions and facilitates the CDMP (Certified Data Management Professional) certification — the internationally recognised competency exam for data management. cimt is a knowledge partner of DAMA NL and actively shares experience from the Dutch practice.
The CDMP certification consists of a mandatory Data Management Fundamentals exam plus two specialisations (e.g. Data Quality, Data Governance, Metadata). For consultants and in-house data professionals it is the most widely accepted individual certification alongside organisation-wide DMBoK adoption.
When DAMA fits
Not every data project needs a complete framework. But in these situations the added value is high.
EU AI Act demands lineage, quality and governance of training data. DMBoK knowledge areas Metadata, Data Quality and Data Governance provide the structure.
Migrating from legacy ETL to Qlik Talend Cloud or a Snowflake lakehouse needs shared vocabulary across Data Architecture and Data Integration — DMBoK provides it.
GDPR, AI Act, sector regulation — if compliance isn't "handled" at the data-management level, incidents keep returning. DMBoK anchors policy in operational knowledge areas.
When growing (data engineer → data team → data department), DMBoK helps to set up roles, responsibilities and RACI structures methodically rather than letting them grow organically.
Merging two companies means merging two data landscapes. A shared DMBoK vocabulary accelerates due diligence and integration.
If different systems hold different customer or product definitions, Reference & Master Data + Data Governance provide the approach to solve it structurally.
Start by measuring
In 2-4 weeks we map your current maturity per DAMA knowledge area — with a prioritised roadmap, quick wins and long-term priorities.
Frequently asked
DMBoK stands for Data Management Body of Knowledge. It is a book (DMBoK v2, 628 pages) plus framework, published by DAMA International, that describes the full discipline of data management in 11 knowledge areas plus Data Governance as a central theme.
A guideline with the status of a de facto industry standard. It is not an ISO norm, but is used worldwide by governments, financial institutions and large enterprises as the reference. Compliance regulators (such as DNB and AFM in the Netherlands) regularly cite it during data-related audits.
DMBoK v2 (2017) is the current published version. v3 is in development with explicit additions for AI governance (model lifecycle, AI ethics), cloud-native architecture and modern data engineering practices. cimt works with v2 and tracks the development towards v3.
DCAM (Data Management Capability Assessment Model) is a commercial maturity model from the EDM Council, popular in financial services. ISO 8000 is an ISO norm specifically for data quality. DMBoK has a broader scope than both and is often used as the overarching framework, with DCAM or ISO 8000 for specific sub-areas. cimt aligns to what you already use.
Typically 2 to 4 weeks for a mid-sized organisation. We assess the current state per knowledge area via interviews (data leads, business owners, IT), document review and data sampling, and deliver a prioritised roadmap with quick wins and long-term priorities. The output is directly usable for budget decisions.
Yes. The methodology scales: at an SME we focus on a handful of knowledge areas with direct business impact (typically Data Governance, Data Quality, Data Integration), while at larger organisations we bring all 11 in scope. The framework is the same — application depth scales with the situation.
A DAMA maturity assessment typically runs between €15k and €40k depending on organisation size and scope. Follow-on engagements (governance implementation, quality monitoring, lineage with erwin Data Intelligence) vary widely. A no-obligation intro call gives a first indication within 60 minutes.
cimt does not offer open-enrolment CDMP exam prep. For in-house knowledge transfer on DMBoK application (workshop, training-on-the-job during an engagement) we regularly make space. For classical CDMP exam preparation we refer to DAMA NL and accredited training providers.