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Imagine prehistoric hunters using stone-tipped spears to hunt large animals like mammoths. As they evolved, these hunters began to analyze their techniques by throwing spears at the skin of a previously killed mammoth, gathering data on which angles and distances were most effective. This process of collecting and analyzing data was crucial for developing better hunting strategies and tools. Similarly, in today’s world, the success of corporations and government institutions hinges on their ability to efficiently collect, organize, and utilize data about various aspects of their operations. Improving data quality is essential, but it doesn’t have to be overwhelming. This book is essential for anyone looking to understand and address data quality issues within their organization. It prioritizes practical solutions over theory, offering a proven approach to identifying, warehousing, and analyzing data errors. Readers will learn techniques in data profiling, metadata gathering, designing data quality rules, and constructing dimensional data quality scorecards. David Wells, Director of Education at the Data Warehousing Institute, praises the book for marking a significant milestone in the evolution of data quality management, transitioning it from an art to a science. Arkady’s insights provide a structured and engaging approach to mastering this discipline.
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Data Quality Assessment, Arkady Maydanchik
- Langue
- Année de publication
- 2007
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- (souple)
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