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Measuring Data Quality for Ongoing Improvement
A Data Quality Assessment Framework
A ready-to-use framework for data quality measurement!
Laura Sebastian-Coleman (Author)
9780123970336, Elsevier Science
Paperback, published 20 February 2013
376 pages
23.4 x 19 x 2.4 cm, 0.75 kg
"This book provides a very well-structured introduction to the fundamental issue of data quality, making it a very useful tool for managers, practitioners, analysts, software developers, and systems engineers. It also helps explain what data quality management entails and provides practical approaches aimed at actual implementation. I positively recommend reading it…" --ComputingReviews.com, January 2014 "The framework she describes is a set of 48 generic measurement types based on five dimensions of data quality: completeness, timeliness, validity, consistency, and integrity. The material is for people who are charged with improving, monitoring, or ensuring data quality." --Reference and Research Book News, August 2013 "If you are intent on improving the quality of the data at your organization you would do well to read Measuring Data Quality for Ongoing Improvement and adopt the DQAF offered up in this fine book." --Data and Technology Today blog, July 2013
The Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You’ll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT and provides practical guidance on how to apply the DQAF within any organization enabling you to prioritize measurements and effectively report on results. Strategies for using data measurement to govern and improve the quality of data and guidelines for applying the framework within a data asset are included. You’ll come away able to prioritize which measurement types to implement, knowing where to place them in a data flow and how frequently to measure. Common conceptual models for defining and storing of data quality results for purposes of trend analysis are also included as well as generic business requirements for ongoing measuring and monitoring including calculations and comparisons that make the measurements meaningful and help understand trends and detect anomalies.
Section One: Concepts and Definitions Chapter 1: Data Chapter 2: Data, People, and Systems Chapter 3: Data Management, Models, and Metadata Chapter 4: Data Quality and Measurement Section Two: DQAF Concepts and Measurement Types Chapter 5: DQAF Concepts Chapter 6: DQAF Measurement Types Section Three: Data Assessment Scenarios Chapter 7: Initial Data Assessment Chapter 8 Assessment in Data Quality Improvement Projects Chapter 9: Ongoing Measurement Section Four: Applying the DQAF to Data Requirements Chapter 10: Requirements, Risk, Criticality Chapter 11: Asking Questions Section Five: A Strategic Approach to Data Quality Chapter 12: Data Quality Strategy Chapter 13: Quality Improvement and Data Quality Chapter 14: Directives for Data Quality Strategy Section Six: The DQAF in Depth Chapter 15: Functions of Measurement: Collection, Calculation, Comparison Chapter 16: Features of the DQAF Measurement Logical Chapter 17: Facets of the DQAF Measurement Types Appendix A: Measuring the Value of Data Appendix B: Data Quality Dimensions Appendix C: Completeness, Consistency, and Integrity of the Data Model Appendix D: Prediction, Error, and Shewhart’s lost disciple, Kristo Ivanov Glossary Bibliography
Subject Areas: Database programming [UMT], Operational research [KJT], Data analysis: general [GPH]
