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Executing data quality projects = ten steps to quality data and trusted information /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Executing data quality projects/ Danette McGilvray.
Reminder of title:	ten steps to quality data and trusted information /
Author:	McGilvray, Danette.
Published:	Amsterdam ;Morgan Kaufmann/Elsevier, : c2008.,
Description:	xviii, 325 p. :ill. ; : 28 cm.;
Subject:	Electronic data processing - Quality control. -
Online resource:	An electronic book accessible through the World Wide Web; click for information
Online resource:	An electronic book accessible through the World Wide Web; click for information
ISBN:	9780123743695

Executing data quality projects = ten steps to quality data and trusted information /
McGilvray, Danette.

Executing data quality projectsten steps to quality data and trusted information /[electronic resource] :Danette McGilvray. - Amsterdam ;Morgan Kaufmann/Elsevier,c2008. - xviii, 325 p. :ill. ;28 cm.

Includes bibliographical references (p. [303]-306) and index.

Introduction -- The Reason for This Book -- Intended Audiences -- Structure of This Book -- How to Use This Book -- Acknowledgements -- Chapter 1 Overview -- Impact of Information and Data Quality -- About the Methodology -- Approaches to Data Quality in Projects -- Engaging Management -- Chapter 2 Key Concepts -- Introduction -- Framework for Information Quality (FIQ) -- Information Life Cycle -- Data Quality Dimensions -- Business Impact Techniques -- Data Categories -- Data Specifications -- Data Governance and Stewardship -- The Information and Data Quality Improvement Cycle -- The Ten StepsT Process -- Best Practices and Guidelines -- Chapter 3 The Ten Steps -- 1. Define Business Need and Approach -- 2. Analyze Information Environment -- 3. Assess Data Quality -- 4. Assess Business Impact -- 5. Identify Root Causes -- 6. Develop Improvement Plans -- 7. Prevent Future Data Errors -- 8. Correct Current Data Errors -- 9. Implement Controls -- 10. Communicate Actions and Results -- Chapter 4 Structuring Your Project -- Projects and The Ten Steps -- Data Quality Project Roles -- Project Timing -- Chapter 5 Other Techniques and Tools -- Introduction -- Information Life Cycle Approaches -- Capture Data -- Analyze and Document Results -- Metrics -- Data Quality Tools -- The Ten Steps and Six Sigma -- Chapter 6 A Few Final Words -- Appendix Quick References -- Framework for Information Quality -- POSMAD Interaction Matrix Detail -- POSMAD Phases and Activities -- Data Quality Dimensions -- Business Impact Techniques -- The Ten StepsT Overview -- Definitions of Data Categories.

Data quality problems cost businesses billions of dollars each year in unnecessary printing, postage, and staffing costs, in the steady erosion of an organization's credibility among customers and suppliers, and the inability to make sound decisions. Danette McGilvray presents a systematic, proven approach to improving data quality by combining a conceptual framework for understanding information quality with techniques and instructions for improving it. The Ten Step approach applies to all types of data and to all types of organizations. * Includes numerous templates, detailed examples, and practical advice for executing every step of The Ten Steps approach. * Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices. * A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from The Ten Step methodology, and other tools and information that is available online.

Electronic reproduction.
Amsterdam :
Elsevier Science & Technology,
2008.

Mode of access: World Wide Web.

ISBN: 9780123743695

Source: 153224:153382Elsevier Science & Technologyhttp://www.sciencedirect.comSubjects--Topical Terms:

596257
Electronic data processing
--Quality control.Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: HD30.2 / .M397 2008eb

Dewey Class. No.: 658.4/04

Executing data quality projects = ten steps to quality data and trusted information /
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300 $a xviii, 325 p. : $b ill. ; $c 28 cm.
504 $a Includes bibliographical references (p. [303]-306) and index.
505 0 $a Introduction -- The Reason for This Book -- Intended Audiences -- Structure of This Book -- How to Use This Book -- Acknowledgements -- Chapter 1 Overview -- Impact of Information and Data Quality -- About the Methodology -- Approaches to Data Quality in Projects -- Engaging Management -- Chapter 2 Key Concepts -- Introduction -- Framework for Information Quality (FIQ) -- Information Life Cycle -- Data Quality Dimensions -- Business Impact Techniques -- Data Categories -- Data Specifications -- Data Governance and Stewardship -- The Information and Data Quality Improvement Cycle -- The Ten StepsT Process -- Best Practices and Guidelines -- Chapter 3 The Ten Steps -- 1. Define Business Need and Approach -- 2. Analyze Information Environment -- 3. Assess Data Quality -- 4. Assess Business Impact -- 5. Identify Root Causes -- 6. Develop Improvement Plans -- 7. Prevent Future Data Errors -- 8. Correct Current Data Errors -- 9. Implement Controls -- 10. Communicate Actions and Results -- Chapter 4 Structuring Your Project -- Projects and The Ten Steps -- Data Quality Project Roles -- Project Timing -- Chapter 5 Other Techniques and Tools -- Introduction -- Information Life Cycle Approaches -- Capture Data -- Analyze and Document Results -- Metrics -- Data Quality Tools -- The Ten Steps and Six Sigma -- Chapter 6 A Few Final Words -- Appendix Quick References -- Framework for Information Quality -- POSMAD Interaction Matrix Detail -- POSMAD Phases and Activities -- Data Quality Dimensions -- Business Impact Techniques -- The Ten StepsT Overview -- Definitions of Data Categories.
520 $a Data quality problems cost businesses billions of dollars each year in unnecessary printing, postage, and staffing costs, in the steady erosion of an organization's credibility among customers and suppliers, and the inability to make sound decisions. Danette McGilvray presents a systematic, proven approach to improving data quality by combining a conceptual framework for understanding information quality with techniques and instructions for improving it. The Ten Step approach applies to all types of data and to all types of organizations. * Includes numerous templates, detailed examples, and practical advice for executing every step of The Ten Steps approach. * Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices. * A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from The Ten Step methodology, and other tools and information that is available online.
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