Essentials of Data Management: DAMA DMBOK Framework Overview

Introduction:

Data management is essential for overseeing an organization’s data throughout its lifecycle using a combination of processes, policies, technologies, and strategies. In today's digital age, where data is growing in volume and complexity, implementing effective data management practices is crucial to ensure data quality, integrity, security, and accessibility.

This course, developed by Balanced Score Training Center is based on the Data Management Body of Knowledge (DMBOK) framework from the Data Management Association (DAMA). It offers a comprehensive guide to the fundamental principles, concepts, and best practices in data management.

The DMBOK framework is a valuable resource for organizations and professionals looking to enhance their data management capabilities. Applying DMBOK principles helps organizations establish a solid foundation for effective data governance, integration, and quality management, ultimately improving decision-making and aligning with strategic goals.

Objectives:

Upon completion of this DAMA DMBOK Course on the Data Management Body of Knowledge Framework, participants will be able to:

• Grasp the main principles of information sharing.
• Develop governance skills.
• Acquire capabilities in database design and data modeling.
• Implement best practices for managing high-quality information.
• Master techniques for data interoperability and integration.
• Gain knowledge in metadata management.
• Understand security and privacy issues concerning databases.
• Enhance understanding of business intelligence and analytics procedures.

Training Methodology:

• Lectures by Subject Matter Experts
• Analysis of Case Studies
• Group Discussions
• Interactive Workshops
• Real-world Scenario Simulations
• Peer Reviews & Feedback
• Continuous Assessments

Course Outline:

Unit 1: Introduction to Data Management and DAMA Framework

• Introduction to Data Management and its importance in organizations.
• Overview of the DAMA framework and its components.
• Understanding the DAMA Data Management Body of Knowledge (DMBOK).
• Fundamental Principles and Concepts of Data Management within the DAMA framework.

Unit 2: Data Governance and Data Architecture

• Data Governance: Establishing a data governance framework and defining roles & responsibilities.
• Data Architecture: Overview and importance in the DAMA framework.
• Data modeling methods and strategies.
• Considerations for data integration and interoperability.

Unit 3: Data Quality and Data Lifecycle Management

• Data Quality Management: Strategies for assessing and improving data quality.
• Techniques for profiling and cleansing datasets.
• Data Lifecycle Management: Understanding lifecycle stages, policies on data retention, archiving, disposal, and destruction.

Unit 4: Data Integration and Master Data Management

• Data Integration: Introduction, challenges, and ETL processes.
• Real-time data integration.
• Master Data Management (MDM): Best practices and its role in data governance.

Unit 5: Data Security and Privacy

• Importance and challenges of data security.
• Policies, controls, and best practices for a secure IT environment.
• Data breaches and incident response management.
• Overview of Data Privacy Regulatory Landscape: Compliance with GDPR, CCPA, and privacy protection principles.