Introduction:
Data management is a critical discipline that encompasses the processes, policies, technologies, and strategies for effectively managing an organization's data assets throughout its lifecycle. With the increasing volume and complexity of data in today's digital age, it has become crucial for businesses to establish robust data management practices to ensure data quality, integrity, security, and accessibility. The Data Management Body of Knowledge (DMBOK) framework, developed by DAMA, provides a comprehensive guide that outlines the fundamental principles, concepts, and best practices for data management. This framework is a valuable resource for organizations and professionals seeking to enhance their data management capabilities and harness the power of data to drive informed decision-making and achieve strategic objectives. By adopting the DMBOK framework, organizations can establish a solid foundation for effective data governance, integration, quality management, and other essential aspects of data management.
Targeted Groups:
- Executives and Senior Management.
- Data Management Practitioners.
- Business Stakeholders.
- IT Professionals.
- Data Consumers.
Course Objectives:
At the end of this course the participants will be able to:
- Understand Data Management Fundamentals.
- Develop Data Governance skills.
- Enhance Data Modeling and Database Design skills.
- Improve Data Quality Management.
- Master Data Integration and Interoperability.
- Gain Proficiency in Metadata Management.
- Build Data Security and Privacy competence.
- Develop Data Analytics and Business Intelligence skills.
Targeted Competencies:
- Introduction to Data Management and DAMA Framework.
- Data Governance and Data Architecture.
- Data Quality and Data Lifecycle Management.
- Data Integration and Master Data Management.
- Data Security and Privacy.
Course Content:
Unit 1: Introduction to Data Management and DAMA Framework:
- Introduction to Data Management and its importance in organizations
- Overview of the DAMA (Data Management Association) framework and its components
- Understanding the DAMA Data Management Body of Knowledge (DMBOK)
- Key principles and concepts of data management within the DAMA framework
Unit 2: Data Governance and Data Architecture:
- Data Governance: Definition, objectives, and benefits
- Establishing a data governance framework within an organization
- Data governance roles and responsibilities
- Data Architecture: Overview and importance within the DAMA framework
- Data modeling techniques and best practices
- Data integration and interoperability considerations
Unit 3: Data Quality and Data Lifecycle Management:
- Data Quality Management: Introduction and significance
- Strategies for assessing and improving data quality
- Data profiling and cleansing techniques
- Data Lifecycle Management: Understanding the data lifecycle stages
- Data retention and archiving policies
- Data disposal and destruction considerations
Unit 4: Data Integration and Master Data Management:
- Data Integration: Introduction and challenges
- Extract, Transform, Load (ETL) processes and tools
- Real-time data integration approaches
- Master Data Management (MDM): Concepts and benefits
- MDM implementation strategies and best practices
- Data governance's role in supporting MDM initiatives
Unit 5: Data Security and Privacy:
- Data Security: Importance and challenges
- Data security policies, controls, and best practices
- Data breaches and incident response management
- Data Privacy: Overview and regulatory landscape
- Ensuring compliance with data privacy regulations (e.g., GDPR, CCPA)
- Implementing privacy by design principles
