undrgrnd Cliquez ici Toys KDP nav-sa-clothing-shoes nav-sa-clothing-shoes Cloud Drive Photos cliquez_ici Cliquez ici Acheter Fire Acheter Kindle Paperwhite cliquez_ici Jeux Vidéo Gifts
Commencez à lire Measuring Data Quality for Ongoing Improvement sur votre Kindle dans moins d'une minute. Vous n'avez pas encore de Kindle ? Achetez-le ici Ou commencez à lire dès maintenant avec l'une de nos applications de lecture Kindle gratuites.

Envoyer sur votre Kindle ou un autre appareil


Essai gratuit

Découvrez gratuitement un extrait de ce titre

Envoyer sur votre Kindle ou un autre appareil

Désolé, cet article n'est pas disponible en
Image non disponible pour la
couleur :
Image non disponible

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

Laura Sebastian-Coleman

Prix conseillé : EUR 41,09 De quoi s'agit-il ?
Prix éditeur - format imprimé : EUR 41,09
Prix Kindle : EUR 28,66 TTC & envoi gratuit via réseau sans fil par Amazon Whispernet
Économisez : EUR 12,43 (30%)

App de lecture Kindle gratuite Tout le monde peut lire les livres Kindle, même sans un appareil Kindle, grâce à l'appli Kindle GRATUITE pour les smartphones, les tablettes et les ordinateurs.

Pour obtenir l'appli gratuite, saisissez votre adresse e-mail ou numéro de téléphone mobile.


Prix Amazon Neuf à partir de Occasion à partir de
Format Kindle EUR 28,66  
Broché EUR 38,21  

Idée cadeau Noël : Retrouvez toutes les idées cadeaux Livres dans notre Boutique Livres de Noël .

Les clients ayant acheté cet article ont également acheté

Cette fonction d'achat continuera à charger les articles. Pour naviguer hors de ce carrousel, veuillez utiliser votre touche de raccourci d'en-tête pour naviguer vers l'en-tête précédente ou suivante.

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
    • Synthèse vocale : Activée
    • X-Ray :
    • Word Wise: Non activé
    • Composition améliorée: Non activé
    • Classement des meilleures ventes d'Amazon: n°321.841 dans la Boutique Kindle (Voir le Top 100 dans la Boutique Kindle)

    En savoir plus sur l'auteur

    Découvrez des livres, informez-vous sur les écrivains, lisez des blogs d'auteurs et bien plus encore.

    Commentaires en ligne

    Il n'y a pas encore de commentaires clients sur Amazon.fr
    5 étoiles
    4 étoiles
    3 étoiles
    2 étoiles
    1 étoiles
    Commentaires client les plus utiles sur Amazon.com (beta)
    Amazon.com: 4.8 étoiles sur 5  4 commentaires
    5 internautes sur 5 ont trouvé ce commentaire utile 
    5.0 étoiles sur 5 Impressive DQAF 2 juillet 2013
    Par Data Guy - Publié sur Amazon.com
    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 internautes sur 2 ont trouvé ce commentaire utile 
    4.0 étoiles sur 5 Essential Reading on Data Quality 27 mai 2014
    Par Anonymous - Publié sur Amazon.com
    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").
    2 internautes sur 2 ont trouvé ce commentaire utile 
    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
    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.
    5.0 étoiles sur 5 but I can already say that I love the approach to the book and the thoroughness of ... 4 juin 2015
    Par Johan Swart - Publié sur Amazon.com
    Format:Format Kindle
    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.
    Ces commentaires ont-ils été utiles ?   Dites-le-nous

    Discussions entre clients

    Le forum concernant ce produit
    Discussion Réponses Message le plus récent
    Pas de discussions pour l'instant

    Posez des questions, partagez votre opinion, gagnez en compréhension
    Démarrer une nouvelle discussion
    Première publication:
    Aller s'identifier

    Rechercher parmi les discussions des clients
    Rechercher dans toutes les discussions Amazon

    Rechercher des articles similaires par rubrique