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Data Quality Assessment (Englisch) Taschenbuch – 13. Juli 2012


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Synopsis

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."

Über den Autor und weitere Mitwirkende

Arkady Maydanchik is a recognized practitioner, author, and educator in the field of data quality and information integration. He is a frequent speaker at conferences and seminars, and teaches data quality courses through the Data Warehousing Institute and through his company, Data Quality Group LLC.

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Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)

Amazon.com: 14 Rezensionen
16 von 16 Kunden fanden die folgende Rezension hilfreich
Hua! It's about time. 1. Februar 2008
Von Geoffrey Hollander - Veröffentlicht auf Amazon.com
Format: Taschenbuch
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.
9 von 10 Kunden fanden die folgende Rezension hilfreich
A Tough Subject Made Easier 18. September 2008
Von Tom Redman - Veröffentlicht auf Amazon.com
Format: Taschenbuch
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.
2 von 2 Kunden fanden die folgende Rezension hilfreich
It's not all about performance... 10. März 2009
Von Frank Kalis - Veröffentlicht auf Amazon.com
Format: Taschenbuch
Managing data most effectivly and analyze that data quick and smart are some of the main requirements for modern database management systems. But what use is all this, when the quality of the underlying raw data is poor?

This book introduces the reader on a very high level precisely, but always easy to understand into several methods and techniques that can be applied to a data quality assessment project. It should be part of the toolbox for everyone concerned about data quality.
2 von 2 Kunden fanden die folgende Rezension hilfreich
Fine book, no fluff 20. März 2010
Von J. Smith - Veröffentlicht auf Amazon.com
Format: Taschenbuch
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!
1 von 1 Kunden fanden die folgende Rezension hilfreich
Handy book 25. November 2010
Von P. ChudaniÄ - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
This book was a good buy from my point of view. I expected that it will have good quality based on other reviews I have read.
Data quality is not fully established domain of knowledge so you can encounter many people with different opinions what data quality really is.
I think that this book very well summarizes knowledge of data quality domain both theoreticaly but more importantly practically. Author is long time practicioner and it is certain that he uses his knowledge troughout the book not just describing some theory unused or impossible to use in practice.

I also appreciate the style of writting. Author is trying to be clear about what he states, gives examples..
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