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Meeting the Challenges of Data Quality Management
- 1st Edition - January 25, 2022
- Author: Laura Sebastian-Coleman
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 1 7 3 7 - 5
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 1 7 5 6 - 6
Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management profe… Read more
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Request a sales quoteMeeting 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.
- Describes the importance of high-quality data to organizations wanting to leverage their data and, more generally, to people living in today’s digitally interconnected world
- Explores the five challenges in relation to organizational data, including "Big Data," and proposes approaches to meeting them
- Clarifies how to apply the core capabilities required for an effective data quality management program (data standards definition, data quality assessment, monitoring and reporting, issue management, and improvement) as both stand-alone processes and as integral components of projects and operations
- Provides Data Quality practitioners with ways to communicate consistently with stakeholders
Data quality engineers, data managers, data analysts, researchers, and engineers who need to ensure consistent, accurate and reliable data across their company, laboratory, or hospital. Graduate students and researchers in data and computer science. Sections 1 and 2 will be of great interest to data governance professionals, data strategists, IT professionals, and chief data officers.
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- In praise of Meeting the Challenges of Data Quality Management
- About the Author
- Foreword
- Acknowledgments
- Introduction: The Challenges of Managing Data Quality
- Why Focus on Data Quality Management?
- Data Quality Management Goals
- Data Quality and the Context of the Organization
- The Five Challenges
- The Structure of This Book
- Why I Wrote This Book
- Section 1: Data in Today’s Organizations
- Section 1. Data in Today’s Organizations
- Chapter 1. The Importance of Data Quality Management
- Abstract
- Introduction
- Data and Value
- The More Things Change, the More They Stay the Same
- Every Organization Is Data-Dependent
- Big Data Is Here
- The Dream of Fully Integrated Data
- The Focus on Volume Distracts from Value
- New Data Opportunities Conflict with Each Other
- The Drive to Improve Data Quality Has Faded
- Organizational Responsibility for the Quality of Data Remains Ambiguous
- Poor-Quality Data Is Costly, Dangerous, and Tolerated
- Meeting the Challenges
- Chapter 2. Organizational Data and the Five Challenges of Managing Data Quality
- Abstract
- Introduction
- The Five Challenges of Managing Data Quality
- Organizational Data
- Organizational Data and Systems Thinking
- The Data Challenge: The Mechanics of Meaning
- The Process Challenge: Managing for Quality
- The Technical Challenge: Data-Technology Balance
- The People Challenge: Knowledge and Data Literacy
- The Culture Challenge: Organizational Responsibility for Data
- Meeting the Challenges
- Chapter 3. Data Quality and Strategy
- Abstract
- Introduction
- Thinking Strategically
- Data Strategy
- Strategic Alignment: People, Process, and Technology
- Assessing Strategic Readiness for Data Quality Management
- Defining the Future State
- Making a Plan
- Section 2: The Five Challenges in Depth
- Section 2. The Five Challenges in Depth
- Chapter 4. The Data Challenge: The Mechanics of Meaning
- Abstract
- Introduction
- Data: A Short History
- What History Teaches Us About Data Quality
- Meeting the Challenge of Understanding Data
- Chapter 5. The Process Challenge: Managing for Quality
- Abstract
- Introduction
- Quality Is Not an Accident
- Definitions of Quality
- Quality Data
- Data as a Product
- The Juran Trilogy: Quality Management Processes
- Dimensions of Product Quality
- Quality Management Principles
- Data Is Different from Other Resources
- Limitations of the Product Model for Data Quality
- Meeting the Process Challenge: Apply Quality Management Principles to Data
- Coda: Build Quality In
- Chapter 6. The Technical Challenge: Data/Technology Balance
- Abstract
- Introduction
- Technology and Data
- Data Is Everywhere
- Information Technology Is Evolving Rapidly
- The Dangers of Technology Hype
- The Tension Between Data and Information Technology
- Codd, the Relational Model, and Data Independence
- Accounting for the Imprint of Technology
- IT Funding Models Contribute to the Tension
- Meeting the Challenges
- Chapter 7. The People Challenge: Building Data Literacy
- Abstract
- Introduction
- A Few Assumptions
- Data About Data Literacy: An Experiment in Observation
- Data Literacy: The Extended Definition
- Data Literacy Skills, Knowledge, Experience
- The Data-Literate Organization
- The Alternative: Data Illiteracy
- Data Literacy and a Growth Mindset
- Meeting the People/Knowledge Challenge: Build Data Literacy
- Coda: Books for the Journey
- Chapter 8. The Culture Challenge: Organizational Accountability for Data
- Abstract
- Introduction
- Accountability, Responsibility, and Good Faith
- Data Requires Oversight
- The Politics of Data Within Organizations
- The Chief Data Officer
- Data Stewardship
- Data Governance
- What’s Wrong with Data Governance?
- Status of the Oversight Problem: Not Solved
- Meeting the Challenges: Improving Data Governance
- Section 3: Data Quality Management Practices
- Section 3. Data Quality Management Practices
- Chapter 9. Core Data Quality Management Capabilities
- Abstract
- Introduction
- Data Quality in the Context of Data Management
- ISO 8000 Part 61: Data Quality Management: Process Reference Model
- The Ten Steps Process: Accounting for Data Quality in Projects
- Core Data Quality Management Capabilities
- Define Data Quality Standards
- Assess Data Quality
- Monitor Data Quality
- Report on Data Quality
- Data Quality Issue Management Overview
- Improve Data Quality
- Applying Core Data Quality Management Capabilities
- Conclusion
- Chapter 10. Dimensions of Data Quality
- Abstract
- Introduction
- Perspectives on the Dimensions of Quality
- Categorizing Dimensions of Quality
- The Meaning Challenge: Choices About Representation
- The Process Challenge: Capturing Metadata
- The Technical Challenge: Technical Processes Affect the Quality of Data
- The People Challenge: Data Consumers Are the Arbiters of Quality
- Concluding Thoughts
- Chapter 11. Data Life Cycle Processes
- Abstract
- Introduction
- The Data Life Cycle and the Asset/Resource Life Cycle
- Managing Quality Throughout the Data Life Cycle
- The Data Supply Chain: Moving Data Into and Within an Organization
- The Value Chain: Finding Efficiencies and Adding Value
- The Systems Development Life Cycle
- Concluding Thoughts
- Chapter 12. Tying It Together
- Abstract
- Glossary
- Bibliography
- Index
- No. of pages: 352
- Language: English
- Edition: 1
- Published: January 25, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780128217375
- eBook ISBN: 9780128217566
LS
Laura Sebastian-Coleman
Laura Sebastian-Coleman, Data Quality Director at Prudential, has been a data quality practitioner since 2003. She has implemented data quality metrics and reporting, launched and facilitated working stewardship groups, contributed to data consumer training programs, and led efforts to establish data standards and manage metadata. In 2009, she led a group of analysts in developing the Data Quality Assessment Framework (DQAF), which is the basis for her 2013 book, Measuring Data Quality for Ongoing Improvement. An active professional, Laura has delivered papers, tutorials, and keynotes at data-focused conferences, such as MIT’s Information Quality Program, Data Governance and Information Quality (DGIQ), Enterprise Data World (EDW), Data Modeling Zone, and Data Management Association (DAMA)-sponsored events. From 2009 to 2010, she served as IAIDQ’s Director of Member Services. In 2015, she received the IAIDQ Distinguished Member Award. DAMA Publications Officer (2015 to 2018) and production editor for the DAMA-DMBOK2 (2017), she is also author of Navigating the Labyrinth: An Executive Guide to Data Management (2018). In 2018, she received the DAMA award for excellence in the data management profession. She holds a CDMP (Certified Data Management Professional) from DAMA, an IQCP (Information Quality Certified Professional) from IAIDQ, a Certificate in Information Quality from MIT, a B.A. in English and History from Franklin & Marshall College, and a Ph.D. in English Literature from the University of Rochester.
Affiliations and expertise
Data Quality Director, Prudential