Certified Data Management Professional (CDMP)

Introduction

The Certified Data Management Professional (CDMP) certification is globally recognized as a mark of excellence in the field of data management. It signifies that an individual possesses the knowledge and skills necessary to excel in data governance, data modeling, database management, data integration, data quality, and master data management. As data continues to be a strategic asset for businesses, CDMP-certified professionals are in high demand for their ability to drive better decision-making, compliance, and data-driven innovation.

Objectives

• Participants will understand the fundamentals of data management, including the importance of data governance, data architecture, and data quality.
• Participants will learn about data governance principles, regulations, and compliance, and be able to define roles and responsibilities within a data governance framework.
• Participants will gain expertise in data modeling, database design, and database management systems, as well as practical skills in using SQL.
• Participants will grasp data integration and ETL processes, including real-time data integration, and will be able to identify and address data integration challenges.
• Participants will learn about data quality assessment, cleansing, and master data management, enabling them to apply data quality strategies and MDM principles in practice.

Course Outline

Day 1

Data Management

• Introduction to CDMP Certification
• The Importance of Data Management
• Data Management Frameworks and Best Practices
• Data Governance and Data Stewardship
• Data Architecture and Data Modelling
• Data Integration and ETL (Extract, Transform, Load)
• Data Quality and Master Data Management
• Case Studies and Real-world Examples

Day 2

Data Governance and Compliance

• Data Governance Principles
• Data Governance Frameworks (e.g., DAMA)
• Data Privacy Regulations (e.g., GDPR, CCPA)
• Role of Data Stewards and Data Owners
• Data Security and Access Control
• Data Compliance and Auditing
• Establishing Data Governance Policies
• Practical Exercises and Group Discussions

Day 3

Data Modelling and Database Management

• Data Modelling Concepts (ERD, Dimensional, etc.)
• Normalization and Denormalization
• Database Design Best Practices
• Data Modelling Tools
• Database Management Systems (DBMS)
• SQL Fundamentals
• Performance Optimization
• Hands-on Database Design Exercises

Day 4

Data Integration and ETL

• Data Integration Approaches
• ETL Processes and Tools
• Data Warehousing Concepts
• Real-time Data Integration
• Data Transformation and Cleaning
• Data Integration Challenges
• Data Loading Strategies
• Practical ETL Exercises

Day 5

Data Quality and Master Data Management (MDM)

• Data Quality Assessment
• Data Profiling and Data Cleansing
• Data Quality Tools
• Introduction to Master Data Management
• Master Data Management (MDM) Principles
• MDM Architecture and Implementation
• Data Governance for MDM
• Case Studies and Best Practices