Data Quality Assessment

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..

Data can be useless or even dangerous if it's of poor quality. And in the course of my daily duties, I have to deal with large volumes of data. To prevent data quality interfering with my work goals, I decided to check the literature on the subject as I haven't ever explicitly read such before. Data Quality Assessment by Maydanchik seemed like a good candidate with excellent Amazon reviews.The book consists of 3 parts. Part one gives a basic overview of the subject and its contents. It answers such questions as what is data quality, what's the structure of a typical data quality project. Part two describes main categories of data quality rules, i.e. checks that verify assumptions about the data. Part three provides tips and strategies for practical implementation of a data quality assessment process.I found the book disappointing from an engineer's perspective. I expected to learn some insights into what is the state of the data quality field, a framework of a data quality project, and useful techniques. While the book does address these goals, it does so on a basic level. Maydanchik aims at managers who have to solve a problem, but don't know about much mathematics and don't have sufficient engineering experience.The level is my main gripe with the book, which is supposed to be technical. The author often takes a long road to explain a simple concept, sometimes even using a metaphor, when just a few words would suffice. For example, an entire paragraph is written to circuitously explain that an operation will not scale because it exhibits quadratic complexity. On the other hand, the text is filled with contrived, precise numbers, which are deceptive as it should be understood that such things as length of the project, acceptable false positive rate, expected speed-up, etc. vary wildly depending on the situation. For example, when discussing sample size, a statement is made to that effect of "because of correlation we won't need 300, but more like 150-200 samples". The arbitrariness of the second range hurts.Perhaps the best way to describe this book is that the content is information thin to people working in the field (note that the book is marketed at people who have to implement such project, not at average Joe). Consider part two, which is, essentially, a presentation of constraints that are commonly implemented and considered by relational databases. I believe that any software engineer should know those at heart. Part three is similar. However, it also contains some good advice here and there. Advice which is good to keep in mind.To summarize, if you are an engineer that is being tasked with or wants to implement a data quality project, then you will find this book lacking of more detailed and actionable advice or methods that you might expect. If on the other hand, relational databases and system infrastructure are things you don't know about, and you might dislike more precise technical books, then this book will provide a satisfactory overview of how a good data quality project might look like.

Very helpful

This is the best book on the subject I have seen, and I read almost all of them!Extremely well organized, taking the reader from the fundamentals through the process in an easy read. He has a way of presenting technical subject in such a way that anyone could understand them. All concepts are accompanied by examples taken from of every-day experience. All the examples are easily understood and well explained. After reading his explanation and examples of even the most complex and obscure data quality issues you are left with the felling of "Oh yea, I get it."

There are a lot of well developed ideas presented in this text. Some of the ideas will be familiar to those who have participated in data quality assessment and repair projects. Nonetheless such a well thought out list is refreshing and sure to add to your understanding.The author has a quirky and fun writing style that makes it a surprisingly entertaining read. Five thumbs up.

This book is so much blah, blah, blah and blah. Not useful, not even for management people. Avoid it.

I am in the middle of setting up a new Data Quality Function now. This is a big help to be in setting up requirements and methodologies and document the processes that our team will follow. A little on the technical side if one is coming from a business background. I found the charts and tables at the back to be a great summary.

This book is both technical and easy to read. Arkady cover the facets of Data Quality easily.I have been reading this book from cover to cover and the book will be my reference book in DQ for years to come.