Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management professionals to help their organizations get more value from data by addressing the five challenges of data quality management: the meaning challenge (recognizing how data represents reality), the process/quality challenge (creating high-quality data by design), the people challenge (building data literacy), the technical challenge (enabling organizational data to be accessed and used, as well as protected), and the accountability challenge (ensuring organizational leadership treats data as an asset). Organizations that fail to meet these challenges get less value from their data than organizations that address them directly.
The book describes core data quality management capabilities and introduces new and experienced DQ practitioners to practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. It extends these ideas to the management of data quality within big data environments. This book will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, as well as academic and government organizations. Readership extends to people higher up the organizational ladder (chief data officers, data strategists, analytics leaders) and in different parts of the organization (finance professionals, operations managers, IT leaders) who want to leverage their data and their organizational capabilities (people, processes, technology) to drive value and gain competitive advantage.
This will be a key reference for graduate students in computer science programs which normally have a limited focus on the data itself and where data quality management is an often-overlooked aspect of data management courses.
1. Introduction: The Challenges of Managing Data Quality - p. Page xv 2. List of Illustrations - p. Page xxviii 3. List of Tables - p. Page xxix 4. Section 1 : Data in Today’s Organizations - p. Page xxix 5. Chapter 1 : The Importance of Data Quality Management - p. Page 2 6. Chapter 2 : Organizational Data and the Five Challenges of Managing Data Quality - p. Page 30 7. Chapter 3 : Data Quality and Strategy - p. Page 45 8. Section 2 : The Five Challenges in Depth - p. Page 65 9. Chapter 4 : The Data Challenge: The Mechanics of Meaning - p. Page 68 10. Chapter 5 : The Process Challenge: Managing for Quality - p. Page 92 11. Chapter 6 : The Technical Challenge: Data/Technology Balance - p. Page 117 12. Chapter 7 : The People Challenge: Building Data Literacy - p. Page 130 13. Chapter 8 : The Culture Challenge: Organizational Accountability for Data - p. Page 164 14. Section 3 : Data Quality Management Practices - p. Page 184 15. Chapter 9 : Core Data Quality Management Capabilities - p. Page 186 16. Chapter 10 : Dimensions of Data Quality - p. Page 228 17. Chapter 11 : Data Life Cycle Processes - p. Page 256 18. Chapter 12 : Tying It Together - p. Page 280 19. Glossary - p. Page 283 20. Bibliography - p. Page 297 21. Index - p. Page 297
Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management professionals to help their organizations get more value from data by addressing the five challenges of data quality management: the meaning challenge (recognizing how data represents reality), the process/quality challenge (creating high-quality data by design), the people challenge (building data literacy), the technical challenge (enabling organizational data to be accessed and used, as well as protected), and the accountability challenge (ensuring organizational leadership treats data as an asset). Organizations that fail to meet these challenges get less value from their data than organizations that address them directly.
The book describes core data quality management capabilities and introduces new and experienced DQ practitioners to practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. It extends these ideas to the management of data quality within big data environments. This book will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, as well as academic and government organizations. Readership extends to people higher up the organizational ladder (chief data officers, data strategists, analytics leaders) and in different parts of the organization (finance professionals, operations managers, IT leaders) who want to leverage their data and their organizational capabilities (people, processes, technology) to drive value and gain competitive advantage.
This will be a key reference for graduate students in computer science programs which normally have a limited focus on the data itself and where data quality management is an often-overlooked aspect of data management courses.
1. Introduction: The Challenges of Managing Data Quality - p. Page xv 2. List of Illustrations - p. Page xxviii 3. List of Tables - p. Page xxix 4. Section 1 : Data in Today’s Organizations - p. Page xxix 5. Chapter 1 : The Importance of Data Quality Management - p. Page 2 6. Chapter 2 : Organizational Data and the Five Challenges of Managing Data Quality - p. Page 30 7. Chapter 3 : Data Quality and Strategy - p. Page 45 8. Section 2 : The Five Challenges in Depth - p. Page 65 9. Chapter 4 : The Data Challenge: The Mechanics of Meaning - p. Page 68 10. Chapter 5 : The Process Challenge: Managing for Quality - p. Page 92 11. Chapter 6 : The Technical Challenge: Data/Technology Balance - p. Page 117 12. Chapter 7 : The People Challenge: Building Data Literacy - p. Page 130 13. Chapter 8 : The Culture Challenge: Organizational Accountability for Data - p. Page 164 14. Section 3 : Data Quality Management Practices - p. Page 184 15. Chapter 9 : Core Data Quality Management Capabilities - p. Page 186 16. Chapter 10 : Dimensions of Data Quality - p. Page 228 17. Chapter 11 : Data Life Cycle Processes - p. Page 256 18. Chapter 12 : Tying It Together - p. Page 280 19. Glossary - p. Page 283 20. Bibliography - p. Page 297 21. Index - p. Page 297
