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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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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
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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.
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Product details
Series: The Morgan Kaufmann Series on Business Intelligence
Paperback: 376 pages
Publisher: Morgan Kaufmann; 1 edition (January 25, 2013)
Language: English
ISBN-10: 0123970334
ISBN-13: 978-0123970336
Product Dimensions:
7.5 x 0.8 x 9.2 inches
Shipping Weight: 1.7 pounds (View shipping rates and policies)
Average Customer Review:
3.8 out of 5 stars
6 customer reviews
Amazon Best Sellers Rank:
#735,415 in Books (See Top 100 in Books)
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.
This book has some good ideas. However, it comes across as an academic research paper rather than trying to convey ideas to folks in normal day-to-day business. It is a VERY difficult read --- to the point that the value is completely lost.
Many systems are developed with Data Quality as an after-thought. The writer clearly outlines the reasons why Data Quality should be thought of as a strategy, not just a one-time activity or the result of using a specific methodology (e.g., Profiling).This should be required reading for Data Quality Practioners, and other related data stakeholders such as Data Architects, Data Modelers, and others who lead data warehousing projects. Those who are in the beginning phases of a project need to understand that this is a shared responsibility and need to structure systems to incorporate appropriate strategies from the onset.The writer has a background in communications and developing web content. Because of this, the writing is well-organized and supremely logical. The book starts with a high-level overview, then drills down to more specific details. For me, it was a little frustrating because I like to jump in "feet first" and get details rapidly. But I found that slowing down and studying the early sections/chapters provided a good foundation for the later material.Be aware that the book may not meet all of your needs and expectations. In the introduction, the author makes an important statement: "...it is important also to point out what the book will not do. It does not, for example, present 'code' for implementing these measurmnents. Although it contains a lot of technically oriented information, it is not a blueprint for a technical implementation. In defining requirements for measurement types, it remains business-oriented and technology independent. It also does not advocate for the use of particular tools."The strength of this book lies in the author's statement: "Many people want to buy tools before they define their goals for measuring. I feel very strongly that people need to know what they are trying to accomplish before they use a tool."So, while this is a great resource for developing a vision and strategy, I still am looking for more information regarding execution of a strategy. There are some recommended books listed, and I've already ordered one of them (Danette McGilvary's book, "Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information").
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.
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.
I'm still busy reading it, but I can already say that I love the approach to the book and the thoroughness of the author.
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