Data Governance, Quality, and Master Data Management (MDM)

Data Analytics and Business Intelligence October 25, 2025
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Introduction

This comprehensive course provides the knowledge and skills needed to establish effective data governance frameworks and implement master data management solutions. Participants will learn to develop data policies, ensure data quality, and manage critical business data assets across the organization. The curriculum covers governance structures, data quality dimensions, MDM implementation strategies, and regulatory compliance requirements. Through practical frameworks and case studies, learners will develop the ability to create sustainable data management practices that support organizational objectives and data-driven decision-making.

Objectives

Key learning objectives include:

  • Design and implement data governance frameworks
  • Establish data quality management processes
  • Implement master data management solutions
  • Develop data policies and standards
  • Create data stewardship programs
  • Measure and monitor data quality metrics
  • Align data governance with business objectives
  • Manage data governance organizational change

Target Audience

  • Data governance managers and officers
  • Data quality analysts and managers
  • IT managers and architects
  • Business analysts and data stewards
  • Compliance and risk officers
  • CDOs and data strategy leaders
  • Project managers implementing MDM

Methodology

The course uses a combination of theoretical frameworks and practical implementation guides. Participants work through real-world scenarios developing governance policies and MDM strategies. Case studies examine successful and failed governance implementations across industries. Group activities focus on collaborative policy development, while individual exercises build assessment and planning skills. Mini-case studies present specific governance challenges, and syndicate discussions explore solution approaches and best practices.

Personal Impact

  • Enhanced ability to design data governance frameworks
  • Improved skills in data quality assessment and improvement
  • Stronger understanding of MDM implementation strategies
  • Increased confidence in developing data policies
  • Better stakeholder management and communication
  • Developed ability to measure governance effectiveness

Organizational Impact

  • Improved data quality and consistency
  • Enhanced regulatory compliance and risk management
  • Better decision-making through trusted data
  • Reduced costs from data quality issues
  • Increased efficiency in data management
  • Stronger data foundation for digital transformation

Course Outline

Unit 1: Data Governance Foundations

Governance Concepts
  • Data governance principles and benefits
  • Governance frameworks and models
  • Roles and responsibilities (stewards, owners)
  • Governance maturity assessment

Unit 2: Data Quality Management

Quality Dimensions
  • Data quality dimensions and metrics
  • Data profiling and assessment
  • Root cause analysis for data issues
  • Data quality monitoring and reporting
Improvement Processes
  • Data cleansing techniques
  • Preventive quality controls
  • Data quality rule development
  • Continuous improvement frameworks

Unit 3: Master Data Management

MDM Concepts
  • Master data domains and characteristics
  • MDM implementation styles
  • Golden record creation
  • Data matching and merging
MDM Implementation
  • MDM architecture and technology
  • Change management for MDM
  • MDM program governance
  • ROI measurement for MDM

Unit 4: Policy and Standards

Framework Development
  • Data policy development
  • Data standards definition
  • Metadata management
  • Data classification schemes

Unit 5: Organizational Implementation

Change Management
  • Stakeholder engagement strategies
  • Communication plans for governance
  • Training and awareness programs
  • Measuring governance effectiveness

Unit 6: Advanced Topics

Specialized Areas
  • Data governance in cloud environments
  • Regulatory compliance requirements
  • Data governance for analytics and AI
  • Emerging trends and technologies

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