Certified Data Management Professional (CDMP) Training Course

SKU: 2038
17 Lesson
|
36 Hours
igmGuru offers the best Certified Data Management Professional (CDMP) training online worldwide. This comprehensive program is designed around the DAMA-DMBOK framework and covers all key areas of modern data management, including data governance, data architecture, data modeling and design, metadata management, data quality, data warehousing and business intelligence, big data, and more. Each module is crafted by industry experts to mirror real-world data management practices and prepare you for the CDMP certification exams. Enroll in the CDMP training today to build in-demand skills, validate your expertise with global certification, and accelerate your career as a data management professional.
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Certified Data-Management Professional Course

Overview

Prerequisites

  • General Knowledge of IT and Data Management
  • Interest in Data Management Career
  • Language Proficiency (English)
  • Data Management Fundamentals (DMF) Exam
  • Specialist Exams (for Practitioner, Master, Fellow)
  • Industry Experience (varies by level: Associate, Practitioner, Master, Fellow)

What will you learn

  • Data Management
  • Data Handling Ethics
  • Data Governance
  • Data Architecture
  • Data Modeling and Design
  • Data Storage and Operations
  • Data Security
  • Data Integration and Interoperability
  • Document and Content Management
  • Reference and Master Data
  • Data Warehousing and Business Intelligence
  • Metadata Management
  • Data Quality
  • Big Data and Data Science
  • Data Management Maturity Assessment
  • Data Management Organization and Role Expectations
  • Data Management and Organizational Change Management

Course Curriculum

1. Introduction
2. Business Drivers
3. Goals
4. Essential Concepts
5. Data
6. Data and Information
7. Data as an Organizational Asset
8. Data Management Principles
9. Data Management Challenges
10. Data Differs from Other Assets
11. Data Valuation
12. Data Quality
13. Planning for Better Data
14. Metadata and Data Management
15. Data Management is Cross-functional
16. Establishing an Enterprise Perspective
17. Accounting for Other Perspectives
18. The Data Lifecycle
19. Different Types of Data
20. Data and Risk
21. Data Management and Technology
22. Effective Data Management Requires Leadership and Commitment
23. Data Management Strategy
24. Data Management Frameworks
25. Strategic Alignment Model
26. The Amsterdam Information Model
27. The DAMA-DMBOK Framework
28. Knowledge Area Context Diagram
29. DMBOK Pyramid (Aiken)
30. DAMA Data Management Framework Evolved
31. DAMA and the DMBOK
1. Introduction
2. Business Drivers
3. Essential Concepts
4. Ethical Principles for Data
5. Principles Behind Data Privacy Law
6. Online Data in an Ethical Context
7. Risks of Unethical Data Handling Practices
8. Establishing an Ethical Data Culture
9. Data Ethics and Governance
1. Introduction
2. Business Drivers
3. Goals and Principles
4. Essential Concepts
5. Activities
6. Define Data Governance for the Organization
7. Perform Readiness Assessment
8. Perform Discovery and Business Alignment
9. Develop Organizational Touch Points
10. Develop Data Governance Strategy
11. Define the DG Operating Framework
12. Develop Goals, Principles, and Policies
13. Underwrite Data Management Projects
14. Engage Change Management
15. Engage in Issue Management
16. Assess Regulatory Compliance Requirements
17. Implement Data Governance
18. Sponsor Data Standards and Procedures
19. Develop a Business Glossary
20. Coordinate with Architecture Groups
21. Sponsor Data Asset Valuation
22. Embed Data Governance
23. Tools and Techniques
24. Online Presence / Websites
25. Business Glossary
26. Workflow Tools
27. Document Management Tools
28. Data Governance Scorecards
29. Implementation Guidelines
30. Organization and Culture
31. Adjustment and Communication
32. Metrics
1. Introduction
2. Business Drivers
3. Data Architecture Outcomes and Practices
4. Essential Concepts
5. Activities
6. Establish Data Architecture Practice
7. Integrate with Enterprise Architecture
8. Tools
9. Data Modeling Tools
10. Asset Management Software
11. Graphical Design Applications
12. Techniques
13. Lifecycle Projections
14. Diagramming Clarity
15. Implementation Guidelines
16. Readiness Assessment / Risk Assessment
17. Organization and Cultural Change
18. Data Architecture Governance
19. Metrics
1. Introduction
2. Business Drivers
3. Goals and Principles
4. Essential Concepts
5. Activities
6. Plan for Data Modeling
7. Build the Data Model
8. Review the Data Models
9. Maintain the Data Models
10. Tools
11. Data Modeling Tools
12. Lineage Tools
13. Data Profiling Tools
14. Metadata Repositories
15. Data Model Patterns
16. Industry Data Models
17. Best Practices
18. Best Practices in Naming Conventions
19. Best Practices in Database Design
20. Data Model Governance
21. Data Model and Design Quality Management
22. Data Modeling Metrics
1. Introduction
2. Business Drivers
3. Goals and Principles
4. Essential Concepts
5. Activities
6. Manage Database Technology
7. Manage Databases
8. Tools
9. Data Modeling Tools
10. Database Monitoring Tools
11. Database Management Tools
12. Developer Support Tools
13. Techniques
14. Test in Lower Environments
15. Physical Naming Standards
16. Script Usage for All Changes
17. Implementation Guidelines
18. Readiness Assessment / Risk Assessment
19. Organization and Cultural Change
20. Data Storage and Operations Governance
21. Metrics
22. Information Asset Tracking
23. Data Audits and Data Validation
1. Introduction
2. Business Drivers
3. Goals and Principles
4. Essential Concepts
5. Activities
6. Identify Data Security Requirements
7. Define Data Security Policy
8. Define Data Security Standards
9. Tools
10. Anti-Virus Software / Security Software
11. HTTPS
12. Identity Management Technology
13. Intrusion Detection and Prevention Software
14. Firewalls (Prevention)
15. Metadata Tracking
16. Data Masking/Encryption
17. Techniques
18. CRUD Matrix Usage
19. Immediate Security Patch Deployment
20. Data Security Attributes in Metadata
21. Metrics
22. Security Needs in Project Requirements
23. Efficient Search of Encrypted Data
24. Document Sanitization
25. Implementation Guidelines
26. Readiness Assessment / Risk Assessment
27. Organization and Cultural Change
28. Visibility into User Data Entitlement
29. Data Security in an Outsourced World
30. Data Security in Cloud Environments
31. Data Security Governance
32. Data Security and Enterprise Architecture
1. Introduction
2. Business Drivers
3. Goals and Principles
4. Essential Concepts
5. Data Integration Activities
6. Plan and Analyze
7. Design Data Integration Solutions , Develop Data Integration Solutions, Implement and Monitor, Tools
8. Data Transformation Engine/ETL Tool, Data Virtualization Server, Enterprise Service Bus, Business Rules Engine
9. Data and Process Modeling Tools, Data Profiling Tool, Metadata Repository, Techniques, Implementation Guidelines
10. Readiness Assessment / Risk Assessment, Organization and Cultural Change, DII Governance
11. Data Sharing Agreements, DII and Data Lineage, Data Integration Metrics
1. Introduction, Business Drivers, Goals and Principles, Essential Concepts, Activities
2. Plan for Lifecycle Management, Manage the Lifecycle, Publish and Deliver Content, Tools
3. Enterprise Content Management Systems, Collaboration Tools, Controlled Vocabulary and Metadata Tools, Standard Markup and Exchange Formats
4. E-discovery Technology, Techniques, Litigation Response Playbook, Litigation Response Data Map
5. Implementation Guidelines, Readiness Assessment / Risk Assessment, Organization and Cultural Change, Documents and Content Governance
6. Information Governance Frameworks, Proliferation of Information, Govern for Quality Content, Metrics
1. Introduction, Business Drivers, Goals and Principles, Essential Concepts, Activities
2. MDM Activities, Reference Data Activities, Tools and Techniques, Implementation Guidelines, Adhere to Master Data Architecture
3. Monitor Data Movement, Manage Reference Data Change, Data Sharing Agreements, Organization and Cultural Change, Reference and Master Data Governance, Metrics
1. Introduction, Business Drivers, Goals and Principles, Essential Concepts, Activities
2. Understand Requirements, Define and Maintain the DW/BI Architecture, Develop the Data Warehouse and Data Marts, Populate the Data Warehouse, Implement the Business Intelligence Portfolio
3. Maintain Data Products, Tools, Metadata Repository, Data Integration Tools, Business Intelligence Tools Types, Techniques
4. Prototypes to Drive Requirements, Self-Service BI, Audit Data that can be Queried, Implementation Guidelines, Readiness Assessment / Risk Assessment, Release Roadmap
5. Configuration Management, Organization and Cultural Change, DW/BI Governance, Enabling Business Acceptance, Customer / User Satisfaction
6. Service Level Agreements, Reporting Strategy, Metrics
1. Introduction, Business Drivers, Goals and Principles, Essential Concepts, Activities
2. Define Metadata Strategy, Understand Metadata Requirements, Define Metadata Architecture, Create and Maintain Metadata, Query, Report, and Analyze Metadata, Tools, Metadata Repository Management Tools,
3. Techniques, Data Lineage and Impact Analysis, Metadata for Big Data Ingest, Implementation Guidelines, Readiness Assessment / Risk Assessment, Organizational and Cultural Change
4. Metadata Governance, Process Controls, Documentation of Metadata Solutions, Metadata Standards and Guidelines, Metrics
1. Introduction, Business Drivers, Goals and Principles, Essential Concepts, Activities
2. Define High Quality Data, Define a Data Quality Strategy, Identify Critical Data and Business Rules, Perform an Initial Data Quality Assessment, Identify and Prioritize Potential Improvements
3. Define Goals for Data Quality Improvement, Develop and Deploy Data Quality Operations, Tools, Data Profiling Tools, Data Querying Tools
4. Modeling and ETL Tools, Data Quality Rule Templates, Metadata Repositories, Techniques, Preventive Actions, Corrective Actions
5. Quality Check and Audit Code Modules, Effective Data Quality Metrics, Statistical Process Control, Root Cause Analysis, Implementation Guidelines, Readiness Assessment / Risk Assessment
6. Organization and Cultural Change, Data Quality and Data Governance, Data Quality Policy, Metrics
1. Introduction, Business Drivers, Principles, Essential Concepts, Activities
2. Define Big Data Strategy and Business Needs, Choose Data Sources, Acquire and Ingest Data Sources, Develop Data Hypotheses and Methods, Integrate / Align Data for Analysis, Explore Data Using Models, Deploy and Monitor, Tools
3. MPP Shared-nothing Technologies and Architecture, Distributed File-based Databases, In-database Algorithms, Big Data Cloud Solutions
4. Statistical Computing and Graphical Languages, Data Visualization Tools, Techniques, Analytic Modeling, Big Data Modeling, Implementation Guidelines, Strategy Alignment
5. Readiness Assessment / Risk Assessment, Organization and Cultural Change, Big Data and Data Science Governance, Visualization Channels Management, Data Science and Visualization Standards, Data Security, Metadata, Data Quality, Metrics
1. Introduction, Business Drivers, Goals and Principles, Essential Concepts, Activities
2. Plan Assessment Activities, Perform Maturity Assessment, Interpret Results, Create a Targeted Program for Improvements, Re-assess Maturity, Tools
3. Techniques, Selecting a DMM Framework, DAMA-DMBOK Framework Use, Guidelines for a DMMA, Readiness Assessment / Risk Assessment, Organizational and Cultural Change, Maturity Management Governance, DMMA Process Oversight, Metrics
1. Introduction, Understand Existing Organization and Cultural Norms, Data Management Organizational Constructs, Decentralized Operating Model, Network Operating Model, Centralized Operating Model, Hybrid Operating Model
2. Federated Operating Model, Identifying the Best Model for an Organization, DMO Alternatives and Design Considerations, Critical Success Factors, Executive Sponsorship, Clear Vision
3. Proactive Change Management, Leadership Alignment, Communication, Stakeholder Engagement, Orientation and Training, Adoption Measurement, Adherence to Guiding Principles, Evolution Not Revolution
4. Build the Data Management Organization, Identify Current Data Management Participants, Identify Committee Participants, Identify and Analyze Stakeholders, Involve the Stakeholders, Interactions Between the DMO and Other Data-oriented Bodies
5. The Chief Data Officer, Data Governance, Data Quality, Enterprise Architecture, Managing a Global Organization, Data Management Roles, Organizational Roles, Individual Roles
1. Introduction, Laws of Change, Not Managing a Change: Managing a Transition, Kotter’s Eight Errors of Change Management
2. Error #1: Allowing Too Much Complacency, Error #2: Failing to Create a Sufficiently Powerful Guiding Coalition, Error #3: Underestimating the Power of Vision, Error #4: Under Communicating the Vision by a Factor of 10, 100, or 1000
3. Error #5: Permitting Obstacles to Block the Vision, Error #6: Failing to Create Short-Term Wins, Error #7: Declaring Victory Too Soon, Error # 8: Neglecting to Anchor Changes Firmly in the Corporate Culture
4. Kotter’s Eight Stage Process for Major Change, Establishing a Sense of Urgency, The Guiding Coalition, Developing a Vision and Strategy, Communicating the Change Vision
5. The Formula for Change, Diffusion of Innovations and Sustaining Change, The Challenges to be Overcome as Innovations Spread, Key Elements in the Diffusion of Innovation, The Five Stages of Adoption
6. Factors Affecting Acceptance or Rejection of an Innovation or Change, Sustaining Change, Sense of Urgency / Dissatisfaction, Framing the Vision, The Guiding Coalition
7. Relative Advantage and Observability, Communicating Data Management Value, Communications Principles, Audience Evaluation and Preparation, The Human Element, Communication Plan, Keep Communicating
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US $ 399.00
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  • Duration : 36 hrs
  • Lifetime Free Upgrade
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US $ 799.00
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  • Duration : 36 Hrs
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Classes Starting From

  • Fast Track Batch 11 Jun 2026
  • Weekday Batch 15 Jun 2026
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CDMP Certification

igmGuru provides a globally recognized Course Completion Certificate upon successful completion of the CDMP Training. This certificate validates your expertise in data governance, data quality, data modeling, and data management best practices, making you job-ready for data management and analytics roles.

CDMP Certification

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