top of page
Abstract Blue Light
Certified Data Management Professional

Target Students​

  • Data management professionals, database administrators, IT professionals, and analysts who are responsible for managing and organizing data within an organization.

Exam Formats​
 

100 multiple-choice questions

Duration :  40 hours (5 days)
Learning Objectives

-Understand the principles and best practices of data management.

-Gain proficiency in data lifecycle management and database design.

-Learn techniques for ensuring data quality, integrity, and security.

-Develop skills in managing and governing data assets effectively.

-Prepare for certification exams and real-world data management challenges.

Exam Options​
 

Online

In-Person

Exam Codes: DMP-804
Exam Duration: 2 hours
Passing Score​​: 70% 
Course Outline
Introduction to Data Management
​
Module 1: Fundamentals of Data Management
​

  • Introduction to Data Management

  • Overview of Data Management Concepts

  • Importance of Data Management in Modern Organizations

  • Key Roles and Responsibilities in Data Management

  • Data Management Frameworks

  • Understanding Data Management Frameworks and Standards

  • Overview of the DAMA-DMBOK (Data Management Body of Knowledge)

  • Implementing Data Management Best Practices

​Module 2: Data Lifecycle Management
​

  • Data Lifecycle Stages

  • Understanding the Data Lifecycle: Creation, Storage, Use, Archiving, and Deletion

  • Best Practices for Managing Data Throughout Its Lifecycle

  • Techniques for Data Archiving and Retention

  • Data Governance in the Lifecycle

  • Integrating Data Governance into the Data Lifecycle

  • Policies and Procedures for Data Management

  • Case Study: Effective Data Lifecycle Management in Large Enterprises

Database Design and Implementation
Module 3: Database Design Principles
​

  • Introduction to Database Design

  • Fundamentals of Database Design: Conceptual, Logical, and Physical Models

  • Understanding Entity-Relationship Diagrams (ERD)

  • Designing Relational Databases: Keys, Normalization, and Integrity Constraints

  • Advanced Database Design

  • Dealing with Complex Data Models: Hierarchies, Networks, and Object-Oriented Databases

  • Introduction to NoSQL Databases: Key-Value Stores, Document Stores, and Graph Databases

  • Case Study: Designing a Database for a Large-Scale Application

Module 4: Database Implementation
​

  • Implementing Databases

  • Setting Up and Configuring Database Management Systems (DBMS)

  • Database Performance Tuning: Indexing, Query Optimization, and Caching

  • Backup and Recovery Strategies for Databases

  • Data Warehousing

  • Introduction to Data Warehousing Concepts

  • Designing and Implementing Data Warehouses

  • ETL (Extract, Transform, Load) Processes for Data Integration

  • Case Study: Implementing a Data Warehouse for Business Intelligence

Data Quality and Integration
Module 5: Data Quality Management
​

  • Importance of Data Quality

  • Understanding Data Quality Dimensions: Accuracy, Completeness, Consistency, Timeliness

  • Data Profiling Techniques for Assessing Data Quality

  • Tools and Technologies for Data Quality Management

  • Data Cleansing and Standardization

  • Techniques for Data Cleansing: Identifying and Correcting Errors

  • Data Standardization and Transformation

  • Case Study: Improving Data Quality in a Multi-System Environment

Module 6: Data Integration Techniques
​

  • Challenges of Data Integration

  • Common Challenges in Integrating Data from Multiple Sources

  • Data Integration Architectures: ETL, ELT, Data Virtualization, and Data Federation

  • Implementing Real-Time Data Integration Solutions

  • Master Data Management (MDM)

  • Introduction to Master Data Management

  • Best Practices for Managing and Governing Master Data

  • Case Study: Implementing MDM in a Large Organization

Data Governance and Security
Module 7: Data Governance Frameworks
​

  • Understanding Data Governance

  • Importance of Data Governance in Ensuring Data Integrity and Compliance

  • Key Components of a Data Governance Framework

  • Roles and Responsibilities in Data Governance

  • Implementing Data Governance

  • Developing Data Governance Policies and Procedures

  • Data Stewardship: Ensuring Accountability and Ownership

  • Case Study: Successful Data Governance Implementation in a Global

Module 8: Data Security and Privacy
​

  • Data Security Fundamentals

  • Understanding Data Security Principles: Confidentiality, Integrity, Availability

  • Implementing Data Security Controls: Encryption, Access Control, and Monitoring

  • Introduction to Data Masking and Tokenization

  • Data Privacy Regulations

  • Overview of Global Data Privacy Regulations: GDPR, CCPA, HIPAA

  • Ensuring Compliance with Data Privacy Laws

  • Case Study: Implementing Data Privacy Measures in a Healthcare Organization

Real-World Applications and Capstone Project
Module 9: Advanced Data Management Topics
​

  • Big Data Management

  • Introduction to Big Data Technologies: Hadoop, Spark, and NoSQL Databases

  • Managing Large Volumes of Data: Scalability, Performance, and Cost Management

  • Case Study: Big Data Management in a Financial Institution

  • Data Ethics and Compliance

  • Ethical Considerations in Data Management

  • Ensuring Compliance with Legal and Regulatory Requirements

  • Developing an Ethical Data Management Strategy

Module 10: Capstone Project and Exam Preparation
​

  • Capstone Project

  • Participants Work on a Comprehensive Data Management Project

  • Application of Skills Learned: Database Design, Data Integration, Governance, and Security

  • Peer Review and Feedback on Project Work

  • Exam Preparation and Review

  • Review of Key Concepts Covered During the Course

  • Sample Exam Questions and Discussion

  • Final Q&A Session and Wrap-Up

bottom of page