An Interactive 5-Day Training Course

An Interactive 5-Day Training Course

Certified Data Management Professional (CDMP)

Certified Data Management Professional (CDMP)

Certified Data Management Professional (CDMP)

Unlock the power of Data Management.

Unlock the power of Data Management.

Description

Certified Data Management Professional (CDMP)

18th - 22nd May 2026

London, UK

$4,950

Welcome to our Certified Data Management (CDMP) public course - a complete, industry aligned program designed to prepare participants for the DAMA® CDMP certification.

Includes:
Hands-on Labs, Templates, Real-World Use Cases, Exam Preparation Resources & Unique AI application for exam preparation.

Classroom Schedule:

Certification Target

This course prepares participants for the DAMA® International CDMP certification (Data Management Fundamentals exam). 

The course content reflects the official 14-topic exam structure and weightings, ensuring learners spend the right amount of time on the most heavily examined areas. 

Course Overview

Organisations today rely on trusted, well-managed data to drive analytics, regulatory compliance, digital transformation, and AI initiatives. Yet many still face challenges such as inconsistent definitions, poor data quality, unclear ownership, and fragmented systems. The CDMP certification provides a globally recognised, vendor-neutral framework for managing data as an enterprise asset, enabling professionals to address these challenges with confidence. 

This course offers a practical, exam-aligned introduction to all 14 CDMP knowledge areas, including governance, quality, modelling, metadata, architecture, MDM, BI/DW, integration, security, and big data. Through structured explanations, hands-on workshops, and real-world examples, learners gain the skills to design and implement effective data management practices that support organisational goals. 

Beyond exam preparation, this program helps participants build the capabilities needed to establish governance structures, improve data quality, manage metadata and lineage, design scalable architectures, and support data-driven decision-making. With focused coverage on the most heavily weighted exam topics, learners finish the course with both the knowledge and confidence required to succeed in the CDMP – Associate exam and contribute meaningful value to their organisations. 

Instructor

We bring senior data leaders as instructors—professionals who combine: 

  • Deep practical experience establishing data governance, quality, and architecture functions 

  • Cross-industry exposure (financial services, government, healthcare, telecom, retail, energy) 

  • Strong grounding in DMBoK® and enterprise best practices 

  • Clear, business-first communication that bridges executives, product, analytics, and IT 

Who Should Attend?

  • Data Managers, Data Governance Leads, Data Stewards 

  • Business/Data Analysts, BI Developers, Data Engineers 

  • Solution/Enterprise Architects 

  • Data Product Managers and Analytics Leaders 

  • Anyone seeking a vendor-neutral foundation in data management and the CDMP - Associate credential 

Prerequisites

  • Familiarity with organisational data/analytics initiatives

  • Basic understanding of databases, metadata, and data lifecycle concepts

  • (Helpful but not required) Exposure to governance, quality, or architecture practices 

Learning Objectives

Upon completion, learners will be able to: 

  • Explain the DMBoK® functional areas and how they integrate into a cohesive data operating model 

  • Establish data governance structures (roles, councils, policies, stewardship, decision rights) 

  • Define and execute data quality controls (dimensions, rules, metrics, issues management) 

  • Model data using conceptual, logical, and physical techniques; align models with business glossaries 

  • Design foundational data architecture (domains, platforms, integration styles, lineage) 

  • Implement metadata management (technical, business, operational) and catalog usage patterns 

  • Plan MDM/Reference Data approaches (domains, matching/merging, mastering patterns) 

  • Address data security & privacy (classification, protection, access, compliance) 

  • Develop roadmaps, business cases, and KPIs for sustainable data management capabilities 

  • Prepare effectively for the CDMP – Associate exam using targeted practice and exam-style drills 

Organisational Impact

Implementing disciplined data management drives measurable benefits across risk, compliance, and value delivery. This course enables teams to: 

  • Increase trust in data via governance, quality, and lineage 

  • Reduce risk and compliance exposure through controls and accountability 

  • Accelerate analytics & AI with standardized, reusable, welldefined data assets 

  • Optimize costs by reducing duplication and rework across data platforms and projects 

  • Improve timetoinsight with curated, cataloged, and productized datasets 

  • Align business & IT via shared definitions, stewardship, and decision rights 

  • Sustain capability with clear operating models, metrics, and continuous improvement 

Certified Data Management Professional (CDMP)

Course Outline:

Day 1

Foundations, Governance & Ethics

1. Data Management Process (2%) 

  • DMBoK overview & functional areas 

  • Data lifecycle and process integration 

  • Operating models, maturity, roles 

2. Data Governance (11%) 

  • Governance councils, stewardship, decision rights 

  • Policies, standards, issue escalation 

  • Data accountability frameworks 

  • Workshop: Drafting a governance RACI and policy set 

3. Data Ethics (2%) 

  • Responsible data use 

  • Privacy, fairness, compliance 

  • Ethical challenges in AI/analytics 

Day 1

Foundations, Governance & Ethics

1. Data Management Process (2%) 

  • DMBoK overview & functional areas 

  • Data lifecycle and process integration 

  • Operating models, maturity, roles 

2. Data Governance (11%) 

  • Governance councils, stewardship, decision rights 

  • Policies, standards, issue escalation 

  • Data accountability frameworks 

  • Workshop: Drafting a governance RACI and policy set 

3. Data Ethics (2%) 

  • Responsible data use 

  • Privacy, fairness, compliance 

  • Ethical challenges in AI/analytics 

Day 1

Foundations, Governance & Ethics

1. Data Management Process (2%) 

  • DMBoK overview & functional areas 

  • Data lifecycle and process integration 

  • Operating models, maturity, roles 

2. Data Governance (11%) 

  • Governance councils, stewardship, decision rights 

  • Policies, standards, issue escalation 

  • Data accountability frameworks 

  • Workshop: Drafting a governance RACI and policy set 

3. Data Ethics (2%) 

  • Responsible data use 

  • Privacy, fairness, compliance 

  • Ethical challenges in AI/analytics 

Day 2

Data Quality, Metadata & Modelling (High-weight topics)

4. Data Quality (11%) 

  • Quality dimensions and rules 

  • Scorecards, dashboards, issue management 

  • Lab: Building a data quality rulebook 

5. Metadata Management (11%) 

  • Business, technical, and operational metadata 

  • Lineage, cataloging, glossary design 

  • Workshop: Creating a glossary and metadata schema 

6. Data Modelling & Design (11%) 

  • Conceptual, logical, physical models 

  • Normalisation, relationships, domains 

  • Lab: Building conceptual and logical models 

Day 2

Data Quality, Metadata & Modelling (High-weight topics)

4. Data Quality (11%) 

  • Quality dimensions and rules 

  • Scorecards, dashboards, issue management 

  • Lab: Building a data quality rulebook 

5. Metadata Management (11%) 

  • Business, technical, and operational metadata 

  • Lineage, cataloging, glossary design 

  • Workshop: Creating a glossary and metadata schema 

6. Data Modelling & Design (11%) 

  • Conceptual, logical, physical models 

  • Normalisation, relationships, domains 

  • Lab: Building conceptual and logical models 

Day 2

Data Quality, Metadata & Modelling (High-weight topics)

4. Data Quality (11%) 

  • Quality dimensions and rules 

  • Scorecards, dashboards, issue management 

  • Lab: Building a data quality rulebook 

5. Metadata Management (11%) 

  • Business, technical, and operational metadata 

  • Lineage, cataloging, glossary design 

  • Workshop: Creating a glossary and metadata schema 

6. Data Modelling & Design (11%) 

  • Conceptual, logical, physical models 

  • Normalisation, relationships, domains 

  • Lab: Building conceptual and logical models 

Day 3

Architecture, Integration, Storage & Operations

7. Data Architecture (6%) 

  • Architecture principles, domains, integration patterns 

  • Data platforms, lakes, warehouses, virtualisation 

8. Data Integration & Interoperability (6%) 

  • ETL/ELT, APIs, messaging, event-driven integration 

  • Interoperability standards and patterns 

9. Data Storage & Operations (6%) 

  • Data lifecycle management 

  • Backup/recovery, archival, performance 

  • Operational governance & SLAs 

  • Lab: Designing storage tiers and lifecycle strategies 

Day 3

Architecture, Integration, Storage & Operations

7. Data Architecture (6%) 

  • Architecture principles, domains, integration patterns 

  • Data platforms, lakes, warehouses, virtualisation 

8. Data Integration & Interoperability (6%) 

  • ETL/ELT, APIs, messaging, event-driven integration 

  • Interoperability standards and patterns 

9. Data Storage & Operations (6%) 

  • Data lifecycle management 

  • Backup/recovery, archival, performance 

  • Operational governance & SLAs 

  • Lab: Designing storage tiers and lifecycle strategies 

Day 3

Architecture, Integration, Storage & Operations

7. Data Architecture (6%) 

  • Architecture principles, domains, integration patterns 

  • Data platforms, lakes, warehouses, virtualisation 

8. Data Integration & Interoperability (6%) 

  • ETL/ELT, APIs, messaging, event-driven integration 

  • Interoperability standards and patterns 

9. Data Storage & Operations (6%) 

  • Data lifecycle management 

  • Backup/recovery, archival, performance 

  • Operational governance & SLAs 

  • Lab: Designing storage tiers and lifecycle strategies 

Day 4

MDM, Reference Data, Big Data, and BI/DW

10. Master & Reference Data Management (10%) 

  • MDM architectures, matching/merging, survivorship 

  • Reference data governance and harmonisation 

  • Workshop: Create a mastering strategy 

11. Big Data (2%) 

  • Big data characteristics and technologies 

  • When to use big data vs. traditional architectures 

12. Data Warehousing & Business Intelligence (10%) 

  • DW architectures (Kimball/Inmon) 

  • Dimensional modelling, semantic layers 

  • Dashboards, KPIs, self-service BI 

  • Lab: Designing a data mart 

Day 4

MDM, Reference Data, Big Data, and BI/DW

10. Master & Reference Data Management (10%) 

  • MDM architectures, matching/merging, survivorship 

  • Reference data governance and harmonisation 

  • Workshop: Create a mastering strategy 

11. Big Data (2%) 

  • Big data characteristics and technologies 

  • When to use big data vs. traditional architectures 

12. Data Warehousing & Business Intelligence (10%) 

  • DW architectures (Kimball/Inmon) 

  • Dimensional modelling, semantic layers 

  • Dashboards, KPIs, self-service BI 

  • Lab: Designing a data mart 

Day 4

MDM, Reference Data, Big Data, and BI/DW

10. Master & Reference Data Management (10%) 

  • MDM architectures, matching/merging, survivorship 

  • Reference data governance and harmonisation 

  • Workshop: Create a mastering strategy 

11. Big Data (2%) 

  • Big data characteristics and technologies 

  • When to use big data vs. traditional architectures 

12. Data Warehousing & Business Intelligence (10%) 

  • DW architectures (Kimball/Inmon) 

  • Dimensional modelling, semantic layers 

  • Dashboards, KPIs, self-service BI 

  • Lab: Designing a data mart 

Day 5

Security, Content Management, Final Integration & Exam Prep

13. Data Security (6%) 

  • Data classification, access controls, encryption 

  • Privacy, compliance, and risk management 

  • Workshop: Mapping controls to sensitivity levels 

14. Document and Content Management (6%) 

  • Unstructured data management 

  • Versioning, retention, digital asset lifecycle 

Day 5

Security, Content Management, Final Integration & Exam Prep

13. Data Security (6%) 

  • Data classification, access controls, encryption 

  • Privacy, compliance, and risk management 

  • Workshop: Mapping controls to sensitivity levels 

14. Document and Content Management (6%) 

  • Unstructured data management 

  • Versioning, retention, digital asset lifecycle 

Day 5

Security, Content Management, Final Integration & Exam Prep

13. Data Security (6%) 

  • Data classification, access controls, encryption 

  • Privacy, compliance, and risk management 

  • Workshop: Mapping controls to sensitivity levels 

14. Document and Content Management (6%) 

  • Unstructured data management 

  • Versioning, retention, digital asset lifecycle 

Exam Information

This course is aligned to the DAMA® International CDMP – Data Management Fundamentals () certification.

Exam Format

  • Computer-based, proctored exam

  • 100 multiple-choice questions

  • 120 minutes duration

  • Closed book

Scoring

  • Passing score determined by DAMA® International

  • Standard Passing Score: 60%

Coverage
The exam evaluates knowledge across the 14 DAMA-DMBOK® knowledge areas, with heavier weighting on:

  • Data Governance

  • Data Quality

  • Data Architecture

  • Data Modelling & Design

  • Metadata Management

Exam Preparation Support (Included in Course)

  • Topic-by-topic practice questions mapped to exam weightings

  • Timed mock exams

  • Exam-taking strategies and common pitfall guidance

  • Personalized readiness checklist

What's Included

  • Expert-led instructing

  • Hands-on labs aligned to the CDMP blueprint 

  • Digital courseware and datasets 

  • Exam preparation guidance (Special new AI preparation) 

  • Official DAMA® CDMP exam

Customisation & Delivery Options

Ideal for: 

Public enrolment 

Corporate teams (customisable schedule and labs) 

Industry-specific adaptations (finance, government, healthcare, retail) 

Let’s supercharge your Emerging Tech Advantage.

Trusted by governments and enterprises across the Middle East to deliver multi-day AI training and strategic enablement programs.

Let’s supercharge your Emerging Tech Advantage.

Trusted by governments and enterprises across the Middle East to deliver multi-day AI training and strategic enablement programs.

Let’s supercharge your Emerging Tech Advantage.

Trusted by governments and enterprises across the Middle East to deliver multi-day AI training and strategic enablement programs.