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Product Description
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 on Amazon.com (beta)
Amazon.com:
5.0 out of 5 stars (9 customer reviews)
11 of 11 people found the following review helpful:
5.0 out of 5 stars
Hua! It's about time., Feb 1 2008
By Geoffrey Hollander - Published on Amazon.com
This review is from: Data Quality Assessment (Perfect Paperback)
My business, Northwest Database Services, has cleaned clients' data for over 20 years. In all that time I've only met two or three people who do this kind of work professionally on a regular basis. (Our conventions are small.)
With this in mind, it is easy to see why I was so pleased and surprised to find someone had written a book about the subject; especially as thoughtful and insightful a one as Quality Data Assessment.
Arkady Maydanchik brings years of experience and first-hand knowledge to the table, while organizing it into a logical, sequential and, most important, understandable manual. This book goes into the typical causes of data degradation as well as how to find it and begin the process of fixing it.
You can't even begin to fix your data until you have a clear picture of what's going on "in there", so data assessment is the first and maybe the most important step in achieving data consistency and reliability. If your work involves data assessment, migration creation or maintenance, you should have this book on your shelf. It's that simple.
But wait, there's more. This is just the first volume in a set of data assessment and cleaning processes, tips, tricks and tools books that will be forthcoming. I'm told that the second volume in this series will be published in October 2008. I know it sounds incredibly geeky, but I can hardly wait.
7 of 7 people found the following review helpful:
5.0 out of 5 stars
A Tough Subject Made Easier, Sep 18 2008
By T. C. Redman - Published on Amazon.com
This review is from: Data Quality Assessment (Perfect Paperback)
Data quality assessment has become so much a part of many data quality programs that we assume that everyone knows how to do it well and efficiently. But this is not the case. Done well, assessment is technically difficult and demanding, often conducted under the press of time, short on budget, and long on conflicting management demands. Much can, and does, go wrong. So Arkady Maydanchik's volume is a welcome addition to the data quality literature. It describes the end-to-end assessment process and each step in a brisk, easy-to-read style.
I especially liked portions of Chapter 8. They lay out a process for actually creating business rules, another one of those nettlesome tasks that people underestimate.
1 of 1 people found the following review helpful:
5.0 out of 5 stars
Fine book, no fluff, Mar 20 2010
By J. Smith - Published on Amazon.com
This review is from: Data Quality Assessment (Perfect Paperback)
Maydanchuk knows his subject well and gives it a thorough treatment.
I bought the book after hearing Maydanchuk speak. Both the speach and the book are well organized and useful.
The book has a bit more than I found useful about how to organize your findings. I use his ideas all the time, but have not used his database at all. Shame on me!
The English is quaint in spots. What the heck!
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