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Measuring Data Quality for Ongoing Improvement

MEASURING DATA QUALITY FOR ONGOING IMPROVEMENT

Information Technology

Computing & Information Technology

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Binding Format

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B2B GT

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B2B & B2C

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.

POST SECONDARY FOUR YEAR

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COLJR

COLSO

COLSR

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Dedication Page.
Contents.
Acknowledgments.
Foreword.
1: Author Biography.
Introduction: Measuring Data Quality for Ongoing Improvement.
2: Concepts and Definitions.
3: Data.
4: Data, People, and Systems.
5: Data Management, Models, and Metadata.
6: Data Quality and Measurement.
7: DQAF Concepts and Measurement Types.
8: DQAF Concepts.
9: DQAF Measurement Types.
10: Data Assessment Scenarios.
11: Initial Data Assessment.
12: Assessment in Data Quality Improvement Projects.
13: Ongoing Measurement.
14: Applying the DQAF to Data Requirements.
15: Requirements, Risk, Criticality.
16: Asking Questions.
17: A Strategic Approach to Data Quality.
18: Data Quality Strategy.
19: Directives for Data Quality Strategy.
20: The DQAF in Depth.
21: Functions of Measurement: Collection, Calculation, Comparison.
22: Features of the DQAF Measurement Logical Model.
23: Facets of the DQAF Measurement Types.
Glossary.
Bibliography.
Index.
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&#x2019;s Lost Disciple, Kristo Ivanov.
Appendix E: Quality Improvement and Data Quality.

Measuring Data Quality for Ongoing Improvement.css-1p8rvzy{position:absolute;padding-left:8px;}

Measuring Data Quality for Ongoing Improvement