Meeting the Challenges of Data Quality Management

Meeting the Challenges of Data Quality Management

1st Edition - February 1, 2022
  • Author: Laura Sebastian-Coleman
  • Paperback ISBN: 9780128217375

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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.

Key Features

  • 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.

Table of Contents

  • Section 1 Data in today’s organizations

    CHAPTER 1 The importance of data quality management

    CHAPTER 2 Organizational data and the five challenges of managing data quality

    CHAPTER 3 Data quality and strategy

    Section 2 The five challenges in depth

    CHAPTER 4 The data challenge: the mechanics of meaning

    CHAPTER 5 The process challenge: managing for quality

    CHAPTER 6 The technical challenge: data/technology balance

    CHAPTER 7 The people challenge: building data literacy

    CHAPTER 8 The culture challenge: organizational accountability for data

    Section 3 Data quality management practices

    CHAPTER 9 Core data quality management capabilities

    CHAPTER 10 Dimensions of data quality

    CHAPTER 11 Data life cycle processes

    CHAPTER 12 Tying It Together

Product details

  • No. of pages: 352
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: February 1, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780128217375

About the Author

Laura Sebastian-Coleman

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