Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation

Introduction

This comprehensive training programme is designed to provide participants with a strong and practical understanding of Data Governance and Data Management, based on DAMA-DMBOK principles and practices.

The programme covers the key areas required to establish, implement, and continuously improve an effective data governance environment, including data management fundamentals, governance frameworks and operating models, data stewardship and ownership, data policies and standards, data quality management, metadata management, data catalogues, governance implementation, and maturity assessment.

The programme emphasizes practical application through case studies, real-world examples, discussions, applied exercises, and organizational scenarios, while maintaining limited emphasis on coding and highly technical topics.

Course Objectives

By the end of this programme, participants will be able to:

• Understand the fundamental principles and practices of enterprise data management.
• Understand and apply the principles of DAMA-DMBOK.
• Understand different Data Governance frameworks and operating models.
• Define data ownership, stewardship, accountability, and responsibilities.
• Develop effective data policies, standards, procedures, and guidelines.
• Apply effective Data Quality Management principles and practices.
• Understand Metadata Management and Data Catalogues.
• Support the implementation of Data Governance programmes within organizations.
• Assess Data Governance maturity and identify governance gaps.
• Develop practical roadmaps for Data Governance improvement.

Course Outlines

Day 1: Data Management Fundamentals & DAMA-DMBOK

1. Introduction to Enterprise Data Management
2. The Importance and Value of Data in Modern Organizations
3. Data Management Principles, Roles and Responsibilities
4. Introduction to the DAMA-DMBOK Framework and Knowledge Areas
5. Applying DAMA-DMBOK Principles in the Workplace
6. Data Management Challenges and Common Organizational Gaps
7. Real-World Examples and Case Studies in Data Management

Day 2: Data Governance Frameworks & Principles

1. Definition, Purpose and Value of Data Governance
2. Key Principles and Components of Data Governance
3. Data Governance Frameworks and Models
4. Decision Rights and Accountability
5. Data Governance Policies, Rules and Controls
6. Data Governance Committees and Councils
7. Real-World Examples of Data Governance Implementation

Day 3: Data Governance Operating Models

1. Understanding Data Governance Operating Models
2. Centralized, Decentralized and Federated Governance Models
3. Organizational Structures for Data Governance
4. Data Governance Roles and Responsibilities
5. Defining Decision Rights and Accountability
6. Coordination Between Business and Technical Functions
7. Case Studies of Data Governance Operating Models

Day 4: Data Stewardship, Ownership & Accountability

1. Principles of Data Stewardship
2. Data Ownership, Responsibility and Accountability
3. Roles and Responsibilities of Data Owners and Data Stewards
4. Data Stewardship Processes and Issue Management
5. Relationships Between Data Owners, Data Stewards and Data Custodians
6. Managing Data Responsibilities Across Organizational Functions
7. Real-World Scenarios in Data Stewardship and Data Ownership

Day 5: Data Policies, Standards & Procedures

1. Data Governance Policy Frameworks
2. Data Policies, Standards, Guidelines and Procedures
3. Developing Enterprise Data Standards
4. Data Classification and Data Handling Requirements
5. Data Management Procedures and Governance Controls
6. Policy Compliance and Monitoring
7. Examples of Enterprise Data Policies and Standards

Day 6: Data Quality Management

1. Fundamentals of Data Quality Management
2. Data Quality Dimensions and Measurement
3. Accuracy, Completeness, Consistency, Timeliness and Validity
4. Data Quality Rules and Controls
5. Data Quality Monitoring and Assessment
6. Data Quality Issue Management and Root-Cause Analysis
7. Data Quality Metrics and Improvement Strategies

Day 7: Metadata Management & Data Catalogues

1. Introduction to Metadata and Its Importance
2. Business, Technical and Operational Metadata
3. Metadata Management Processes and Responsibilities
4. Metadata Standards and Governance
5. Data Catalogues: Purpose, Structure and Capabilities
6. Business Glossaries, Data Dictionaries and Data Lineage
7. Practical Examples of Metadata Management and Data Catalogues

Day 8: Data Governance Implementation

1. Moving from Data Governance Strategy to Implementation
2. Data Governance Implementation Stages
3. Prioritizing Data Domains and Governance Initiatives
4. Governance Processes, Workflows and Controls
5. Change Management and Stakeholder Engagement
6. Measuring Data Governance Performance and Adoption
7. Developing a Data Governance Implementation Roadmap

Day 9: Data Governance Maturity & Continuous Improvement

1. Introduction to Data Governance Maturity
2. Data Governance Maturity Assessment Models
3. Data Governance Maturity Levels and Dimensions
4. Assessing Current Capabilities and Identifying Gaps
5. Establishing Governance Improvement Priorities
6. Governance KPIs, Metrics and Performance Monitoring
7. Developing a Data Governance Maturity Improvement Roadmap

Day 10: Integrated Data Governance & Strategic Application

1. Integrating Data Management and Data Governance
2. Connecting Governance, Data Quality, Metadata and Data Stewardship
3. Common Challenges in Data Governance Implementation
4. Integrated Case Study: Enterprise Data Governance
5. Analysis of Real-World Data Governance Scenarios
6. Developing a Sustainable Data Governance Strategy
7. Developing a Data Governance Action Plan and Roadmap

Training Methodology

The programme will use a practical and interactive learning approach, including:

• Case studies
• Real-world examples
• Organizational scenarios
• Applied exercises
• Guided discussions
• Problem and challenge analysis
• Practical application of concepts
• Review of international best practices

Why Attend This Course? Wins & Losses!

Wins

• Gain a comprehensive understanding of Data Governance and Data Management.
• Apply DAMA-DMBOK principles within the workplace.
• Understand Data Governance frameworks and operating models.
• Clearly define Data Owner and Data Steward responsibilities.
• Develop effective data policies, standards and procedures.
• Improve organizational Data Quality practices.
• Understand Metadata Management and Data Catalogues.
• Gain practical knowledge for implementing Data Governance programmes.
• Assess Data Governance maturity and identify governance gaps.
• Develop practical plans and roadmaps for continuous improvement.

Losses of Not Attending

• Continued gaps in data ownership and accountability.
• Inconsistent data policies and standards across the organization.
• Ongoing data quality issues.
• Limited management of metadata and data catalogues.
• Difficulty implementing effective and sustainable Data Governance.
• Limited ability to assess Data Governance maturity.
• Increased operational and decision-making risks resulting from poorly managed and governed data.

Conclusion

This 10-day, 40-hour training programme provides comprehensive coverage of Data Governance and Data Management, beginning with DAMA-DMBOK principles and data management fundamentals and progressing through governance frameworks, operating models, data stewardship and ownership, policies and standards, data quality, metadata management, data catalogues, implementation, and maturity assessment.

Through case studies, real-world examples, organizational scenarios, discussions, and applied exercises, participants will develop the knowledge and practical capabilities required to support and enhance Data Governance practices within their organizations.