Module 1 – Data Management Foundations

01 – Business Drivers for Data Management
02 – Core Concepts: Data, Information, Principles & Challenges
03 – Data as a Strategic Enterprise Asset
04 – Data Management Strategy & Operating Vision
05 – Frameworks Overview & the DMBOK Pyramid

Module 2 – Data Ethics & Responsible Data Use

06 – Business Drivers for Ethical Data Management
07 – Ethical Principles in Data Usage
08 – Foundations of Data Privacy & Protection Laws
09 – Risks & Impacts of Unethical Data Practices
10 – Building an Ethical, Data-Driven Culture
11 – Integrating Data Ethics with Governance

Module 3 – Data Governance

12 – Data Governance Overview: Drivers, Objectives & Value
13 – Core Principles of Effective Data Governance
14 – Governance Readiness & Maturity Assessment
15 – Policies, Standards, Stewardship & Accountability
16 – Embedding Governance into Business Operations
17 – Issue, Exception & Change Management
18 – Governance Tools, Techniques & KPIs
19 – Governing the Data Governance Function

Module 4 – Data Architecture

20 – Business & Technology Drivers
21 – Architecture Goals, Outcomes & Best Practices
22 – Architecture Activities & Key Deliverables
23 – Alignment with Enterprise Architecture (EA)
24 – Architecture Tools & Techniques
25 – Implementation & Transition Guidelines
26 – Architecture Governance & Metrics

Module 5 – Data Modeling & Design

27 – Objectives of Data Modeling
28 – Conceptual, Logical & Physical Modeling Concepts
29 – Modeling Lifecycle: Plan, Build, Review & Maintain
30 – Modeling, Metadata & Lineage Tools
31 – Design Standards & Best Practices
32 – Version Control & Model Governance
33 – Metrics & Alignment with Data Governance

Module 6 – Data Storage & Operations

34 – Overview: Drivers, Scope & Objectives
35 – Data Storage Concepts & Structures
36 – Database Technologies & Administration
37 – Operational Management & Monitoring
38 – Tools & Operational Techniques
39 – Implementation & Optimization Guidelines
40 – Storage Governance & Performance Metrics

Module 7 – Data Security

41 – Security Drivers & Threat Landscape
42 – Security Goals, Concepts & Controls
43 – Security Policies & Regulatory Requirements
44 – Access Control, Encryption & Data Protection
45 – Auditing, Logging & Continuous Monitoring
46 – Secure Implementation Practices
47 – Security Governance & Risk Metrics

Module 8 – Data Integration & Interoperability

48 – Integration Drivers & Business Needs
49 – Integration Concepts & Target Outcomes
50 – Integration Planning & Analysis
51 – Design & Development of Integration Pipelines
52 – Implementation, Testing & Monitoring
53 – Tools & Techniques: ETL, ELT, ESB & Virtualization
54 – Integration Governance & Metrics

Module 9 – Document & Content Management

55 – Content Management Drivers
56 – ECM Concepts & Information Lifecycle
57 – Content Lifecycle Planning
58 – Content Lifecycle Execution & Control
59 – Publishing, Access & Delivery
60 – Tools & Techniques: ECM, Collaboration, e-Discovery
61 – Content Governance & Compliance Metrics

Module 10 – Reference & Master Data Management

62 – MDM & Reference Data Drivers
63 – Core Concepts & Business Value
64 – Master Data Management Architectures
65 – Golden Records, Hierarchies & Survivorship
66 – MDM Tools & Technologies
67 – Implementation & Rollout Guidelines
68 – MDM Governance & Quality Metrics

Module 11 – Data Warehousing & Business Intelligence

69 – BI & Analytics Drivers
70 – DW & BI Concepts
71 – Business Intelligence Needs Assessment
72 – Data Warehouse Architecture Design
73 – Data Development, Loading & Population
74 – Operations, Maintenance & Optimization
75 – BI & DW Tools & Platforms
76 – Analytics Governance & Metrics

Module 12 – Metadata Management

77 – Metadata Drivers & Use Cases
78 – Metadata Concepts & Types
79 – Metadata Strategy & Requirements Definition
80 – Metadata Architecture & Integration
81 – Metadata Creation & Maintenance
82 – Metadata Delivery, Discovery & Consumption
83 – Metadata Tools & Repositories
84 – Metadata Governance & Metrics

Module 13 – Data Quality Management

85 – Data Quality Drivers & Business Impact
86 – Data Quality Dimensions & Concepts
87 – Defining & Measuring High-Quality Data
88 – Data Quality Assessment Techniques
89 – Data Cleansing & Improvement Processes
90 – Operationalizing Data Quality
91 – Data Quality Tools & Automation
92 – Quality Governance & Performance Metrics

Module 14 – Big Data & Data Science

93 – Big Data & Advanced Analytics Drivers
94 – Big Data Principles & Architectures
95 – Big Data Strategy & Use-Case Prioritization
96 – Data Ingestion, Processing & Pipelines
97 – Analytics, Modeling & Hypothesis Testing
98 – Deployment, Monitoring & Scaling
99 – Tools & Platforms: MPP, Distributed DBs & Visualization
100 – Big Data Governance & Metrics

Module 15 – Data Management Maturity Assessment

101 – Maturity Assessment Drivers
102 – Maturity Models & Concepts
103 – Assessment Planning & Scope Definition
104 – Execution of the Maturity Assessment
105 – Results Interpretation & Gap Analysis
106 – Improvement Roadmap & Action Planning
107 – Reassessment, Benchmarking & Continuous Improvement
108 – Assessment Governance & Metrics

Module 16 – Data Management Organization & Roles

109 – Data Management Organizational Overview
110 – Culture, Behavior & Operating Norms
111 – Operating Models: Centralized, Federated & Hybrid
112 – Critical Success Factors
113 – Building & Scaling the DM Organization
114 – Roles, Responsibilities & Skills
115 – Interaction with CDO, DG & EA Functions
116 – Organizational Governance & Metrics

Module 17 – Data Management & Change Management

117 – Change Management Overview for Data Programs
118 – Laws & Dynamics of Change
119 – Managing Transition vs. Organizational Change
120 – Kotter’s 8 Common Change Failures
121 – Kotter’s 8-Step Change Framework
122 – Sustaining Adoption & Behavioral Change
123 – Communicating Data Management Value
124 – Change Governance & Success Metrics

Module 18 – CDMP Exam Guide & Capstone Review

125 – Consolidated Review, Key Concepts, Practice Guidance & Exam Strategy

Appendix 1A – Strategic Alignment Model (SAM)

Appendix 1B – Amsterdam Information Model (AIM)

Appendix 1C – DAMA-DMBOK Framework Overview

Appendix 1D – The DMBOK Pyramid (Aiken)

Appendix 1E – DAMA Data Management Framework (Evolved – Sue Geuens)

Appendix 1F – DAMA and the DMBOK Relationship

Appendix 1G – DAMA Wheel (Evolved)

✅ DAMA DMBoK 2.0 – All 17 Chapters

01. Introduction to Data Management
02. Data Handling Ethics
03. Data Governance
04. Data Architecture
05. Data Modeling & Design
06. Data Storage & Operations
07. Data Security
08. Data Integration & Interoperability
09. Document & Content Management
10. Reference & Master Data
11. Data Warehousing & Business Intelligence
12. Metadata Management
13. Data Quality
14. Big Data & Data Science
15. Data Management Maturity Assessment
16. Data Management Organization & Role Expectations
17. Data Management & Organizational Change Management

Learn the fundamentals of data management and the DAMA-DMBOK2 framework. Understand the CDMP certification structure, data management principles, and how data strategy, lifecycle, and governance integrate across organizations.
Outcome: Build conceptual clarity and align your understanding with DAMA’s 17 knowledge areas.

Explore data ethics, responsible data use, privacy regulations (GDPR, DPDP, CCPA), and the moral implications of data decisions. Analyze ethical dilemmas in financial and AI use cases.
Outcome: Apply ethical frameworks to ensure trust, compliance, and accountability in data-driven environments.

Master the structure, roles, policies, and operating models of enterprise data governance. Learn about stewardship, ownership, RACI models, and governance maturity frameworks like DCAM.
Outcome: Design a functional governance framework ready for BFS and fintech organizations.

Understand how conceptual, logical, and physical data architectures support business goals. Explore frameworks (TOGAF, Zachman), data lineage, integration patterns, and architecture governance.
Outcome: Create architecture blueprints connecting data design, integration, and analytics.

Learn data modeling principles: normalization, denormalization, relational and dimensional models. Practice modeling for customer, account, and transaction data in banking.
Outcome: Gain modeling fluency required for CDMP and real-world data warehouse projects.

Study database operations, backups, archiving, cloud storage, and performance tuning. Understand retention and storage policies critical in regulated industries.
Outcome: Design scalable, secure, and compliant storage solutions

Explore the confidentiality, integrity, and availability triad. Learn encryption, tokenization, masking, and RBAC/ABAC access models.
Outcome: Ensure sensitive data is governed, protected, and auditable.

Understand data movement, interoperability, and orchestration. Learn about ETL, ELT, APIs, and streaming frameworks like Kafka.
Outcome: Design seamless data pipelines for analytics, risk, and reporting platforms.

Learn document lifecycle management, metadata tagging, version control, and retention for unstructured data. Explore BFS use cases such as loan documentation and KYC content.
Outcome: Implement compliant document and content management practices

Master the concepts of golden records, MDM styles (registry, consolidation, coexistence), data matching, and stewardship.
Outcome: Design and govern MDM frameworks for customer and product domains.

Understand DW architectures, dimensional modeling, OLAP concepts, and BI analytics. Learn with practical BFS dashboards and KPI frameworks.
Outcome: Build end-to-end DW/BI pipelines that deliver actionable insights.

Discover how metadata links business, technical, and operational perspectives. Learn cataloging, glossary creation, and lineage visualization with tools like Collibra and Alation.
Outcome: Implement a metadata-driven data management ecosystem.

Master data quality dimensions, profiling, cleansing, enrichment, and DQ dashboards. Learn practical rules for KYC, AML, and risk reporting datasets.
Outcome: Build and monitor enterprise data quality frameworks.

Understand big data technologies (Hadoop, Spark, Databricks), ML data preparation, and AI ethics. Learn governance patterns for large-scale and ML datasets.
Outcome: Integrate big data management with traditional DMBOK practices.

Learn to assess your organization’s maturity using DAMA-DMM, DCAM, and CMMI. Identify gaps and define roadmaps toward a data-driven culture.
Outcome: Conduct data management maturity assessments for organizations.

Define roles of the CDO, stewards, custodians, and governance committees. Learn how data organizations scale in BFS and enterprise contexts.
Outcome: Establish organizational structures for successful data management programs.

Learn stakeholder engagement, communication planning, and change enablement strategies for data governance programs.
Outcome: Enable sustainable data management transformation within your organization.

100+ practice questions, full mock exam, and preparation tips for CDMP Fundamentals & Practitioner exams. Includes exam pattern, time management, and scoring insights.