Data Quality: The Field GuideDigital Press, 2001 - 241 pagina's Can any subject inspire less excitement than "data quality"? Yet a moment's thought reveals the ever-growing importance of quality data. From restated corporate earnings, to incorrect prices on the web, to the bombing of the Chinese Embassy, the media reports the impact of poor data quality on a daily basis. Every business operation creates or consumes huge quantities of data. If the data are wrong, time, money, and reputation are lost. In today's environment, every leader, every decision maker, every operational manager, every consumer, indeed everyone has a vested interest in data quality. Data Quality: The Field Guide provides the practical guidance needed to start and advance a data quality program. It motivates interest in data quality, describes the most important data quality problems facing the typical organization, and outlines what an organization must do to improve. It consists of 36 short chapters in an easy-to-use field guide format. Each chapter describes a single issue and how to address it. The book begins with sections that describe why leaders, whether CIOs, CFOs, or CEOs, should be concerned with data quality. It explains the pros and cons of approaches for addressing the issue. It explains what those organizations with the best data do. And it lays bare the social issues that prevent organizations from making headway. "Field tips" at the end of each chapter summarize the most important points.
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Inhoudsopgave
Who Cares About Data Quality? | 1 |
Yes Millie Even CEOs Are Interested in Data Quality | 3 |
Internet Users Wonder Are These Prices Correct? and DotComs Better Make Sure They Are | 7 |
Chief Financial Officers and Managers of Ongoing Operations Need to Know Where the Money Is | 15 |
Marketers Need to Know about Their Customers | 21 |
Chief Information Officers Are Stuck in the Middle | 27 |
Just in Case You Didnt See Yourself in Chapters 15 | 35 |
The Business Case for Data Quality | 37 |
Statistical Control Establishing a Basis for Prediction | 123 |
Quality Improvement Root Cause Analysis to Uncover the Real Causes of Error | 131 |
Quality Planning Setting Targets for Improvement | 137 |
Quality Planning Designing New Information Chains | 141 |
A Note on Reengineering | 147 |
Middle Management Roles and Responsibilities | 151 |
Data Supplier Management | 153 |
Managing Information Chains | 161 |
Disasters Played Out in Public | 39 |
Poor Data Quality Can Be Insidious | 43 |
Seek Competitive Advantage Through Quality Data | 47 |
The Heart of the Matter | 51 |
A Database Is Like a Lake | 53 |
Likely Outcomes | 57 |
The Organic Nature of Data | 61 |
Crafting the Approach | 65 |
Necessary Background | 69 |
Data and Data Quality Defined | 71 |
SecondGeneration Data Quality Systems | 75 |
The CustomerSupplier Model | 95 |
Blocking and Tackling | 99 |
Understanding Customer Needs After All They Are the Final Arbiters of Quality | 101 |
Better Faster Cheaper | 107 |
Measurement 2 Data Tracking | 113 |
Edit Controls | 119 |
Making Better Decisions | 167 |
Tools | 171 |
Why Senior Management Must Lead and What It Must Do | 175 |
Senior Leadership and Support | 177 |
Crafting a Data Policy | 181 |
Organizing for Data Quality | 187 |
Recognizing Social Issues | 189 |
Advancing the Data Culture | 197 |
Summaries | 203 |
On and Just Over the Horizon | 205 |
Field Tips Reorganized | 209 |
The United States Elections of 2000 | 217 |
Glossary | 221 |
References | 229 |
Instructions for Downloading Figures and Tables | 231 |
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Overige edities - Alles bekijken
Veelvoorkomende woorden en zinsdelen
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