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1.2 The DMBoK Framework

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DAMA Certified Data Management Professional CDMP

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1 Chapter 1: Exam Overview & DMBoK Framework

1.1 CDMP Exam Details & Strategy 1.2 The DMBoK Framework 1.3 Data Management Organization & Roles

2 Chapter 2: Data Governance (Part A)

2.1 Data Governance Principles & Drivers 2.2 Operating Models & Structures 2.3 Policies, Standards, and Procedures

3 Chapter 2: Data Governance (Part B)

2.4 Data Stewardship 2.5 Governance Metrics & Implementation

4 Chapter 3: Data Modeling and Design (Part A)

Section 3.1: Conceptual, Logical, and Physical Models Section 3.2: ERD Notations & Relational Modeling Section 3.3: Normalization

5 Chapter 3: Data Modeling and Design (Part B)

3.4 Dimensional Modeling 3.5 Naming Standards & Modeling Best Practices

6 Chapter 4: Data Quality (Part A)

Section 4.1: Data Quality Dimensions Section 4.2: Data Quality Policy & Rules Section 4.3: DMAIC & DQ Improvement Lifecycle

7 Chapter 4: Data Quality (Part B)

4.4 Root-Cause Analysis & Remediation 4.5 DQ Metrics, Tools, and Reporting

8 Chapter 5: Metadata Management (Part A)

5.1 Business, Technical, and Operational Metadata 5.2 Metadata Lineage & Impact Analysis 5.3 Repository Architectures

9 Chapter 5: Metadata Management (Part B)

5.4 Standards and Semantics 5.5 Metadata Governance & Integration

10 Chapter 6: Master and Reference Data Management

6.1 Master vs Reference Data 6.2 MDM Repository Architectures 6.3 Match-Merge & Identity Resolution 6.4 Reference Data Governance & Standards

11 Chapter 7: Data Warehousing and Business Intelligence

7.1 DW Architectures: Inmon vs Kimball 7.2 Data Warehousing Storage & Load Patterns 7.3 BI Delivery & OLAP 7.4 Data Marts & Enterprise Data Warehouses

12 Chapter 8: Data Architecture

8.1 Enterprise Data Architecture & Zachman Framework 8.2 Data Flow Diagrams & Value Chains 8.3 Architecture Frameworks & Standards

13 Chapter 9: Document and Content Management

9.1 Records Management & ECM 9.2 Retention, Disposal, and E-Discovery 9.3 ARMA GARP Principles & Taxonomies

14 Chapter 10: Data Integration and Interoperability

Section 10.1: Integration Patterns & Technologies Section 10.2: Replication & Virtualization Section 10.3: Canonical Models & Integration Standards

15 Chapter 11: Data Security

Section 11.1: Security Principles: CIA Triad & Privacy Section 11.2: Access Control & Encryption Section 11.3: Security Regulations & Standards

16 Chapter 12: Data Storage and Operations

Section 12.1: Database Technologies & DBA Roles Section 12.2: Backup, Recovery, and High Availability Section 12.3: Performance Tuning & Operations

17 Chapter 13: Big Data and Data Science

13.1 Big Data V's & Technologies

18 Chapter 14: Data Ethics

14.1 DMBoK Data Ethics Framework

19 Chapter 15: Data Management Process and Maturity

15.1 Maturity Assessment Frameworks

1.2 The DMBoK Framework

Key Takeaways Explain This Topic AI

Data Governance sits at the center of the DAMA Wheel, acting as the coordinating hub for the other 10 technical disciplines.

The DAMA Environmental Factors Hexagon consists of Goals & Principles, Activities, Deliverables, Roles & Responsibilities, Practices & Techniques, and Technology (Tools).

DAMA Context Diagrams define the inputs, activities, deliverables, controls, supporters, consumers, and metrics for each Knowledge Area.

Activities in the DAMA framework are categorized into Plan, Develop, Control, and Operate phases.

Last updated: July 2026

1.2 The DMBoK Framework

The DAMA-DMBOK2 framework provides a comprehensive, structured approach to data management. Rather than viewing data management as a purely technical IT discipline, the framework approaches it as a business capability that spans the entire organization. To standardize this capability, the DMBOK2 uses two primary visual models: the DMBoK Wheel and the Environmental Factors Hexagon (which forms the core of the Context Diagram for each Knowledge Area).

The DMBoK Wheel

The DMBoK Wheel is the most recognizable diagram in the DAMA framework. It defines the 11 core Knowledge Areas of data management and visualizes their relationship. +-----------------------------------+

  • | DATA ARCHITECTURE |
  • +-----------------------------------+
  • |
  • +-----------------------------------+
  • | DATA MODELING & DESIGN |
  • +-----------------------------------+
  • |
  • +-----------------------------------+
  • | DATA STORAGE & OPERATIONS |
  • +-----------------------------------+
  • |
  • +-----------------------------------+
  • | DATA SECURITY |
  • +-----------------------------------+
  • |
  • +-----------------------------------+
  • | DATA INTEGRATION & INTEROPER. |
  • +-----------------------------------+
  • |
  • +-------------------------------------------------------+
  • | DATA GOVERNANCE |
  • | (Center Hub / Control) |
  • +-------------------------------------------------------+
  • |
  • +-----------------------------------+
  • | DOCUMENT & CONTENT MANAG. |
  • +-----------------------------------+
  • |
  • +-----------------------------------+
  • | REFERENCE & MASTER DATA |
  • +-----------------------------------+
  • |
  • +-----------------------------------+
  • | DATA WAREHOUSING & BI |
  • +-----------------------------------+
  • |
  • +-----------------------------------+
  • | METADATA |
  • +-----------------------------------+
  • |
  • +-----------------------------------+
  • | DATA QUALITY |
  • +-----------------------------------+
  • Why Governance is the Hub

At the center of the DMBoK Wheel sits Data Governance . Data Governance is not a peripheral activity; it is the hub that drives, governs, and coordinates the other 10 technical disciplines (the "spokes" of the wheel). Without central governance to establish policies, standards, and strategic direction, the technical disciplines operate in silos, leading to inconsistent data quality, redundant storage, and security vulnerabilities.

The 11 Knowledge Areas Defined

For the CDMP exam, you must know the exact DAMA definitions for each of the 11 Knowledge Areas:

Data Governance : Planning, oversight, and control over the management and use of data.

Data Architecture : Defining the blueprint for managing data assets, aligning data strategy with business strategy.

Data Modeling and Design : Discovering, analyzing, representing, and communicating data requirements in precise models.

Data Storage and Operations : Deploying, administering, and recovery of databases and physical storage systems.

Data Security : Ensuring data privacy, confidentiality, and preventing unauthorized access or data breaches.

Data Integration and Interoperability : Consolidating, moving, and transforming data across applications, systems, and platforms.

Document and Content Management : Managing unstructured data, including files, documents, records, and media.

Reference and Master Data : Managing shared core enterprise entities (e.g., customer, product, location) and standardizing code lists.

Data Warehousing and Business Intelligence : Storing, processing, and providing analytical access to history data for decision-making.

Metadata Management : Collecting, integrating, and providing access to descriptions of data assets (data about data).

Data Quality : Measuring, monitoring, and improving the trustworthiness, completeness, and accuracy of data.

Exam Tip: Keep in mind that while the Wheel depicts 11 core Knowledge Areas, the CDMP exam syllabus includes additional chapters from the DMBOK2, such as Data Ethics, Big Data & Data Science, and Data Management Maturity Assessment (DMMA), bringing the total number of tested domains to 14/15.

The Environmental Factors Hexagon

While the DMBoK Wheel shows what areas must be managed, the Environmental Factors Hexagon explains how each Knowledge Area is structured and operationalized. DAMA recognizes that successful data management requires aligning people, processes, and technologies. [Goals & Principles]

  • / \\
  • [Activities] / \\ [Deliverables]
  • | K.A. |
  • [Roles & | | [Practices &
  • Responsibilities] \\ / Techniques]
  • \\ /
  • [Technology / Tools]

The six environmental factors form a hexagon surrounding each Knowledge Area in the DMBOK2 Context Diagrams:

Goals and Principles : The strategic drivers and guiding beliefs of the Knowledge Area. These answer why we manage this data asset and what principles (e.g., data quality, security by design) govern our actions.

Activities : The actions and processes required to meet the goals. DAMA categorizes these activities into four distinct phases:

Plan : Establishing strategy, policies, and designs.

Develop : Building, coding, and implementing systems.

Control : Monitoring, auditing, and enforcing standards.

Operate : Day-to-day administration, data loading, and operations.

Deliverables : The tangible outputs, assets, or artifacts created by the activities. Examples include data models, data lineage maps, quality scorecards, or access logs.

Roles and Responsibilities : The human organizational component. This defines who performs the work, who is accountable, and who must be consulted or informed (typically mapped via a RACI matrix).

Practices and Techniques : The common methodologies, workflows, standards, and procedural steps used to perform the activities.

Technology (Tools) : The software, hardware, and infrastructure platforms that enable and automate the activities (e.g., metadata repositories, modeling software, ETL tools).

Organization and Culture : The management systems, change management strategies, and internal culture that influence how individuals treat data as an asset.

Reading a DAMA Context Diagram

For every Knowledge Area chapter, the DMBOK2 provides a standard Context Diagram . You must understand how to read these diagrams, as they map the inputs and outputs of the environmental factors:

Inputs : The physical or digital assets (like raw data, business requirements, or source documents) needed to start the activities.

Supporters and Drivers : The roles that actively perform or fund the work.

Controls : The laws, regulations, and policies that restrict or govern the activities (e.g., GDPR, CCPA, corporate data security policies).

Metrics : Quantitative indicators used to measure the quality, efficiency, and success of the activities.

Consumers : The end-users or systems that utilize the deliverables produced by the Knowledge Area.