DMBK3: Data Quality Management

CDMP Fundamentals

The course covers Data Quality Management within the DAMA Body of Knowledge (DMBoK®) in depth. It discusses the many different parts of Data Quality and why validity is often confused with quality. It discusses the dimensions of Data Quality as well as the policies, procedures, metrics, technology, and resources for ensuring Data Quality. It looks at a specific reference model, as well as root cause analysis, tools that helps support Data Quality Management, creation and monitoring of Data Quality measures, and various myths and pitfalls around Data Quality Management.

Purpose

To understand the importance, position, and role of Data Quality for organizations and data professionals.

Outcome

• Understand the various Data Quality areas
• Explore Data Quality policies and procedures
• Discuss data profiling, validation rules, facets, data cleansing, and more
• Understand how Data Quality is linked to other data disciplines
• Learn about the DMBoK dimensions of Data Quality

• Data Quality Management
  • Data Quality Management and the DAMA DMBoK Wheel
  • Goals and Business Drivers of Data Quality Management
• Data Quality Overview
  • Data Quality Key Points and Definitions
  • What is Data Quality Management?
  • Scenarios of Data Quality Management
  • The Impact of Poor Data
  • The Body of Knowledge for Data Quality Management
  • The Data Quality Management Approach
  • A Simple Framework for Data Quality Improvement
• Measuring and Data Profiling
  • Measuring Data Quality
  • Assessing Data Quality Through Profiling
  • Typical Outputs of Data Quality Profiling
  • Validation Rules of Based Monitoring
  • Data Quality Monitoring in DQM Framework
• Tools and Techniques
  • Good Data Quality vs. Poor Data Quality
  • Data Quality Facets (DMBoK)
  • The Path to Accuracy
  • Understanding Data Correction
  • Demystifying Data Cleansing
  • How Good Does Data Quality Need to Be?
  • Don’t Just Fix the Data
• Impacts and Dimensions
  • Costs and Efforts of Business Impact
  • The Many Dimensions of Data Quality
  • Data Quality Facets (DMBoK)
  • DMBoK Dimensions of Data Quality
  • Applying Data Quality Dimensions
  • The Business Rules of Data Quality
  • Are Data Governance and Data Quality Linked?
  • Measuring Data Quality
  • The Path to Accuracy
  • Characteristics of Data Quality indicators (DQI)
  • S/W Tools: Functional DQ Capabilities
• Root Cause Analysis
  • Root Cause Analysis (RCA) Problems and Remediation
  • What is Root Cause Analysis?
  • Associated Causes of Root Cause Analysis
  • The Root Cause Analysis Process
• Approaches, Assessments, and Roadmap
  • The 5-Why Approach
  • What is Theory of Constraints?
  • Common Data Quality Mistakes
  • Financial Cost Associated with Data Quality
  • Creating the Data Quality Roadmap
  • Maturity Assessment (Overall) of Data Quality
• Wrap Up
  • Key Takeaways