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

By:

McGilvray, Danette [author]

Material type: Text

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9780123743695 (alk. paper)
0123743699 (alk. paper)

Subject(s):

DDC classification:

658.404 22 M.D.E

LOC classification:

HD30.2 .M397 2008

Online resources:

Table of contents only

Contents:

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.

Summary: 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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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Books	Main library B7	Commerce and business administration ( HR Management )	658.404 M.D.E (Browse shelf(Opens below))	Available		00009181

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.

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