Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework

Other | December 31, 2012

byLaura Sebastian-Coleman, Laura Sebastian-coleman

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The Data Quality Assessment Frameworkshows 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.



  • Demonstrates how to leverage a technology independent data quality measurement framework for your specific business priorities and data quality challenges
  • Enables discussions between business and IT with a non-technical vocabulary for data quality measurement
  • Describes how to measure data quality on an ongoing basis with generic measurement types that can be applied to any situation

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From the Publisher

The Data Quality Assessment Frameworkshows 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, consis...

Laura Sebastian-Coleman, a data quality architect at Optum Insight, has worked on data quality in large health care data warehouses since 2003. Optum Insight specializes in improving the performance of the health system by providing analytics, technology and consulting services. Laura has implemented data quality metrics and reporting,...

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Measuring Data Quality For Ongoing Improvement: A Data Quality Assessment Framework
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Paperback|Jan 11 2013

$66.21 online$68.95list price
Format:OtherDimensions:376 pages, 1 × 1 × 1 inPublished:December 31, 2012Publisher:Morgan KaufmannLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0123977541

ISBN - 13:9780123977540

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Extra Content

Table of Contents

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