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Data Quality Assessment

Autor Arkady Maydanchik
en Limba Engleză Paperback – mai 2007
Imagine a group of prehistoric hunters armed with stone-tipped spears. Their primitive weapons made hunting large animals, such as mammoths, dangerous work. Over time, however, a new breed of hunters developed. They would stretch the skin of a previously killed mammoth on the wall and throw their spears, while observing which spear, thrown from which angle and distance, penetrated the skin the best. The data gathered helped them make better spears and develop better hunting strategies. Quality data is the key to any advancement, whether it is from the Stone Age to the Bronze Age. Or from the Information Age to whatever Age comes next. The success of corporations and government institutions largely depends on the efficiency with which they can collect, organise, and utilise data about products, customers, competitors, and employees. Fortunately, improving your data quality does not have to be such a mammoth task. This book is a must read for anyone who needs to understand, correct, or prevent data quality issues in their organisation. Skipping theory and focusing purely on what is practical and what works, this text contains a proven approach to identifying, warehousing, and analysing data errors. Master techniques in data profiling and gathering metadata, designing data quality rules, organising rule and error catalogues, and constructing the dimensional data quality scorecard. David Wells, Director of Education of the Data Warehousing Institute, says "This is one of those books that marks a milestone in the evolution of a discipline. Arkady's insights and techniques fuel the transition of data quality management from art to science -- from crafting to engineering. From deep experience, with thoughtful structure, and with engaging style Arkady brings the discipline of data quality to practitioners."
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Specificații

ISBN-13: 9780977140022
ISBN-10: 0977140024
Pagini: 321
Ilustrații: b/w illus
Dimensiuni: 178 x 254 x 23 mm
Greutate: 0.59 kg
Editura: Technics Publications

Cuprins

Causes of Data Quality Problems; Data Quality Program Overview; Data Quality: Assessment Overview; Attribute Domain Constraints; Relational Integrity Rules; Rules for Historical Data; Rules for State-Dependent Objects; Attribute Dependency Rules; Implementing Data Quality Rules; Fine-Tuning Data Quality Rules; Cataloguing Errors; Measuring Data Quality Scores; Data Quality Meta Data Warehouse; Recurrent Data Quality Assessment; Index.