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Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework (The Morgan Kaufmann Series on Business Intelligence)
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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
- Sales Rank: #676128 in Books
- Brand: Brand: Morgan Kaufmann
- Published on: 2013-01-25
- Released on: 2013-01-11
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .82" w x 7.50" l, 1.65 pounds
- Binding: Paperback
- 376 pages
- Used Book in Good Condition
Review
"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
From the Back Cover
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.
About the Author
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, launched and facilitated Optum Insight’s Data Quality Community, contributed to data consumer training programs, and has led efforts to establish data standards and manage metadata. In 2009, she led a group of analysts from Optum and UnitedHealth Group in developing the original Data Quality Assessment Framework (DQAF) which is the basis for Measuring Data Quality for Ongoing Improvement.
An active professional, Laura has delivered papers at MIT’s Information Quality Conferences and at conferences sponsored by the International Association for Information and Data Quality (IAIDQ) and the Data Governance Organization (DGO). From 2009-2010, she served as IAIDQ’s Director of Member Services.
Before joining Optum Insight, she spent eight years in internal communications and information technology roles in the commercial insurance industry. She holds the IQCP (Information Quality Certified Professional) designation 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).
Most helpful customer reviews
5 of 6 people found the following review helpful.
Impressive DQAF
By Data Guy
Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework by Laura Sebastian-Coleman (Morgan Kaufmann, ISBN: 978-0-12-397033-6) offers a ready-to-use framework for data quality measurement. Using the information in this book you can establish meaningful data quality measurements that will work across data storage systems and products. It helps to define appropriate controls that will contribute to improving the quality of data at any organization.
The book is divided into six sections. The first focuses on the concepts and definitions necessary to set the stage for the remainder of the topics. Section two introduces the DQAF (Data Quality Assessment Framework) and section three walks through data assessment scenarios. Section four of the book applies the DQAF to data requirements and section five discusses data strategy.
It is in section six where the DQAF is defined in depth. Functions and features of the DQAF are presented and then the final chapter offers the coup de grace, defining the 6 facets and 48 measurement types that comprise the DQAF.
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.
2 of 2 people found the following review helpful.
Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework
By Cynthia J. Hartman
As a business person, this book has forever changed the way I look at data, as well as my perspective on data that’s presented to me. When reading this book, 2 points in particular impressed me.
First, it’s true that Business and Technical (IT) people tend to view data differently. IT people tend towards the technical aspects of the data, whereas Business people don’t care so much about the numbers as they do about what the numbers say in terms of business impact. Sebastian-Coleman hits home on the “meeting of the minds” between IT and Business people so they can work together to get the most out of their data.
Second, the “data quality dimensions” are a key component in understanding the quality of your data. However, they are conceptual in nature and can be difficult to relate to, particularly for Business people. The DQAF (Data Quality Assessment Framework) outlined in this book presents a dissection of the “Data Quality Dimensions” into a practical, generic “menu” that can serve as a great starting point for any company to begin developing a set of measurements integral to a good data quality program.
This book is a “must read” for anyone - IT or Business - working in the data space today.
1 of 1 people found the following review helpful.
this is a really great book, especially if you're working in healthcare
By Kristy Cahoon
Overall, this is a really great book, especially if you're working in healthcare. While there are data quality assessment frameworks available without buying an expensive book, the author has done a great job of making it relevant and useful (and not available on the internet so you have to buy the book).
Generally the book is easy to follow and comes with a lot of useful information. My one complaint is that there should be a website where you can pull diagrams and charts from the book (with license restrictions, of course), so that if I want to take them into a meeting and show them I don't have to recreate them from scratch.
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