Quality data ensures accurate and timely information for effective management. Dr Laura Sebastian-Coleman, author of Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework, acknowledges the challenge of precisely defining and measuring quality data. Consequently, the text presents a generalizable framework for data quality assessment. This text interjects generic and understandable criteria into the ongoing discussion about dimensions of data quality. A data quality practitioner with experience in a range of data quality improvement and data governance efforts, Dr Sebastian-Coleman realizes that without understandable criteria, efforts to improve data quality will be unsustainable.
This book introduces the Data Quality Assessment Framework (DQAF) as a solution to problems of data quality sustainability. The DQAF is a set of 48 generic measurement types based on five dimensions of data quality: completeness, timeliness, validity, consistency, and integrity. Each measurement type represents a category within a particular dimension of data quality that allows for a repeatable pattern of measurement to be used with any data that fits the criteria required by its type. As a result, actual data content does not impact the assessment of its quality.
The DQAF is constructed so as to better define expectations about data. The DQAF subsequently helps the reader understand why and how to measure critical data. Each measurement type is defined by six facets: a comprehensive definition, a set of business concerns that the measurement type addresses, a measurement methodology, a set of engineering or programming considerations, a description of support processes needed for the measurement type, and a set of logical attributes needed to define specific metrics and to store the results of these measurements. According to the author, these criteria established by the DQAF serve to provide guidance for organizations in building quality measurements into their processes. This book subsequently explores the accepted functions as well as prospective capacities of data throughout its chapters.
The text remains, however, a framework within which other data processes occur. Rather than explicitly address how to conduct process or root cause analysis, or measure the value of data or the costs of poor data quality, this text essentially serves as a prerequisite to these functions of data quality assessment. Nevertheless, the concepts presented in the text allow readers to define data quality expectations more clearly, readily enabling them to reap the benefits of any selected data quality tool.
Measuring Data Quality for Ongoing Improvement represents a critical shift in the approach to data quality measurement, such that it synthesizes recent advancements in tools and concepts related to data profiling into an understandable framework for data quality measurement and monitoring. The text promulgates a deeper understanding of data quality measurement and a practical approach to applying that understanding. This book is thereby accessible to any reader who to some capacity engages professionally in the direct proving and sustaining of data quality, including data quality practitioners, systems analysts, software developers and engineers, and IT executives.
About the reviewer
Krista Engemann is a graduate student at the Hagan School of Business, Iona College, New Rochelle, New York, USA
