Data Quality: The Field GuideElsevier Science, 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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business rules Chapter complete control charts cost customer needs customer requirements data and information data clean-up data council data customer data model data quality efforts data quality improvement data quality management data quality program data quality system data record data resource data Data supplier management data values data warehouse database datum define departments dimension of data end-to-end error rate example Field Guide Field Tip Figure Harvard Business School high-quality data identify impact implement important dimension improve data quality Information Age information chain management information chain owner information products information technology involves issues Joseph Juran measurement ments Motivation/Advantages non-value-added organization organization's organizational p-chart Pareto charts poor data quality presented problems quality levels reengineering requirements and feedback responsibility for data root cause analysis Second-Generation Characteristics selected senior management simple sources Statistical process control statistical quality control steps successful data quality summary tion tomer
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