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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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Most Helpful Customer Reviews
2 of 2 people found the following review helpful:
5.0 out of 5 stars
Finally - a practical guide for DQ practitioners,
By
This review is from: Data Quality Assessment (Perfect Paperback)
For many years DQ awareness has improved incrementally with several key authors all adding layers of knowledge and experience through various publications.
This, however, is the first book so far that dispenses with the often irrelevant theory and instead actually gives the reader a straightforward account of how to deliver a DQ assessment, the very thing the industry has been crying out for all this time! An extremely thorough yet highly readable book. There is something in here for the DQ beginner through to the experienced practitioner. This book is long overdue within the DQ industry and should be essential reading for anybody who wants to help their organisation or clients take their DQ strategy in the right direction. Excellent throughout.
1 of 1 people found the following review helpful:
4.0 out of 5 stars
A valuable contribution,
By Amsterdamned (Amsterdam, The Netherlands) - See all my reviews
This review is from: Data Quality Assessment (Perfect Paperback)
This is a excellent and practical guide to data quality assessment for those working in large (American) corporations with plenty of data warehouses, legacy tables and data quality problems; and with the human and financial resources to tackle them. Though similar data quality principles apply regardless of the amount of data you have, the practical side of this book really won't suit those working in smaller organisations, with less data and fewer resources.
The book needed a more stringent and knowledgeable editor/proof-reader, and I would have liked to have seen a little less of the author in the book - the constant references to books he will write get irritating. I also fervantly hope that the comment that you can only be a data quality expert if you have attended one of his seminars or read his book was made with his tongue firmly in his cheek! Overal, a very valuable contribution.
0 of 1 people found the following review helpful:
3.0 out of 5 stars
Overview of techniques,
By
This review is from: Data Quality Assessment (Perfect Paperback)
More than just a data quality assessment approach, this book offers you an overview of concepts that can be applied in the realm of data quality improvement.
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