About the Book

Having worked as a data quality practitioner for the past 15 years, I have noticed a significant change in the ways that we can approach data quality management. Data quality is rapidly transitioning from an industry dominated by simplistic approaches to name and address cleansing to one that more closely mirrors a business productivity management environment. The growing recognition that high quality data more efficiently fuels the achievement of business objectives implies that the need to develop an enterprise data quality program.

But in order to build this program, one needs more than name and address cleansing tools. Instead, one needs the basic policies, processes, and maturity that contribute to the management and governance framework for maintaining measurably high-quality data. My new book, “The Practitioner’s Guide to Data Quality Improvement” is intended to provide the fundamentals for developing the enterprise data quality program, and is intended to guide both the manager and the practitioner in establishing operational data quality control throughout an organization, with particular focus on:

  • The ability to build a business case for instituting a data quality program;
  • Assessing levels of data quality maturity;
  • The guidelines and techniques for evaluating data quality and identifying metrics related to the achievement of business objectives;
  • The techniques for measuring, reporting, and taking action based on these metrics; and
  • The policies and processes used in exploiting data quality tools and technologies for data quality improvement

My goal for this book is to help those individuals tasked with roles in areas such as data quality, data governance, master data management, customer data integration, as well as a host of other data management roles succeed in these types of activities:

  • Building a business case for establishing a data quality program
  • Developing a strategy for enterprise data quality management, data governance, and data stewardship
  • Developing an implementation plan
  • Roles and responsibilities
  • Developing policies and procedures for data quality assessment, data quality metrics, and ongoing monitoring and reporting
  • Using data quality tools and technology
  • Data standards management
  • Data quality tracking and performance trending