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A professional-level summary covering key definitions, frameworks, and exam-relevant points.
DAMA DMBOK v2: Structure and Exam Relevance
The DAMA Data Management Body of Knowledge (DMBOK) version 2 is the authoritative reference framework for the data management profession. Published in 2017, it defines 14 knowledge areas, each representing a distinct discipline within the broader field of data management. The CDMP exam is structured entirely around these 14 areas.
Knowledge Area Exam Weightings
| Knowledge Area | Approx. Exam Weight | Key Concepts |
|---|---|---|
| Data Governance | 11% | Policies, roles, decision rights, councils |
| Data Quality | 11% | 6 dimensions, DQ lifecycle, profiling |
| Data Modeling and Design | 11% | Conceptual/logical/physical models, normalisation |
| Metadata Management | 11% | Business/technical/operational metadata, lineage |
| Master Data Management | 10% | Golden record, MDM styles, survivorship |
| Data Warehousing and BI | 10% | Kimball, Inmon, star schema, SCD types |
| Data Architecture | 6% | Enterprise architecture, data flows, blueprints |
| Data Storage and Operations | 6% | DBMS types, storage technologies, operations |
| Data Security | 6% | Access control, encryption, privacy, compliance |
| Data Integration and Interoperability | 6% | ETL, ELT, APIs, data virtualisation |
| Documents and Content | 6% | ECM, unstructured data, content lifecycle |
| Reference and Master Data | 6% | Reference data types, MDM vs RDM |
| Data Management Process Improvement | 6% | CMMI, maturity models, process metrics |
| Big Data and Data Science | 6% | Hadoop, Spark, ML, data science lifecycle |
The DMBOK Wheel
The DMBOK represents its knowledge areas as a wheel, with Data Governance at the hub — reflecting its role as the overarching discipline that provides authority and direction for all other knowledge areas. This architectural metaphor is important for the exam: governance is not one of 14 equal areas; it is the integrating framework that gives coherence to all the others.
Study Prioritisation Strategy
Given the exam weighting, candidates should allocate approximately 55% of their study time to the five highest-weighted areas (Data Governance, Data Quality, Data Modeling, Metadata Management, Master Data Management) and distribute the remaining 45% across the other nine areas. This weighting-adjusted approach maximises expected score improvement per hour of study.