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. - 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

Autorentext

Laura Sebastian-Coleman, Data Quality Lead at Aetna/CVS Heath, has worked on data quality in large health care data warehouses since 2003. Laura has implemented data quality metrics and reporting, launched and facilitated working stewardship groups, contributed to data consumer training programs, and led efforts to establish data standards and manage metadata. In 2009, she led a group of analysts in developing the Data Quality Assessment Framework (DQAF) which is the basis for her 2013 book, Measuring Data Quality for Ongoing Improvement.
An active professional, Laura has delivered papers, tutorials, and keynotes at data-focused conferences (MIT's Information Quality Program, DGIQ-Data Governance and Information Quality, EDW-Enterprise Data World, Data Modeling Zone, and DAMA-Data Management Association sponsored events). From 2009-2010, she served as IAIDQ's (now Information Quality International) Director of Member Services. In 2015, she received the IAIDQ Distinguished Member Award. DAMA Publications Officer (2015-18) and production editor for the DAMA-DMBOK2 (2017), she is also author of Navigating the Labyrinth: An Executive Guide to Data Management (2018). In 2018, she received the DAMA award for excellence in the data management profession. She holds the CDMP (Certified Data Management Professional) from DAMA, the IQCP (Information Quality Certified Professional) from IAIDQ, a Certificate in Information Quality from MIT, a B.A. in English and History from Franklin & Marshall College, and Ph.D. in English Literature from the University of Rochester (NY).

Klappentext

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.

• 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

Inhalt

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