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Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework
 
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Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework [Format Kindle]

Laura Sebastian-Coleman

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Descriptions du produit

Revue de presse

"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 30, 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 2, 2013

Présentation de l'éditeur

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

    Détails sur le produit

    • Format : Format Kindle
    • Taille du fichier : 3369 KB
    • Nombre de pages de l'édition imprimée : 376 pages
    • Editeur : Morgan Kaufmann; Édition : 1 (31 décembre 2012)
    • Vendu par : Amazon Media EU S.à r.l.
    • Langue : Anglais
    • ASIN: B00AWOTAQE
    • Synthèse vocale : Activée
    • X-Ray :
    • Classement des meilleures ventes d'Amazon: n°284.116 dans la Boutique Kindle (Voir le Top 100 dans la Boutique Kindle)
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    5.0 étoiles sur 5 Impressive DQAF 2 juillet 2013
    Par Data Guy - Publié sur Amazon.com
    Format:Broché
    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.
    4.0 étoiles sur 5 Essential Reading on Data Quality 27 mai 2014
    Par Anonymous - Publié sur Amazon.com
    Format:Broché
    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").
    5.0 étoiles sur 5 Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework 4 février 2014
    Par Cynthia J. Hartman - Publié sur Amazon.com
    Format:Broché
    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.
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