Customer Reviews

Enterprise Knowledge Management: The Data Quality Approach (The Morgan Kaufmann Series in Data Management Systems)

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Poor data quality has a profound effect on our everyday lives - consider the 2000 Presidential election and the Florida recount nightmare. Yet, the extent of poor data quality can be effectively measured and therefore, controlled, when we apply process management, technology, and good old common sense!

"Bad data" has traditionally been masked in terms of curious anecdotes and curious stories that propagate through an organization. Yet, poor data quality has a serious effect on a company's bottom line, especially when bad data propagates out to the customer via incorrect billing, wrong delivery addresses, public relations nightmares, etc.

In my experience consulting on data management projects, I noticed many patterns associated with data quality problems. In this book, I try to address both the management issues as well as the technical issues associated with the different kinds of problems, and I try to provide a framework for capturing the knowledge embedded in data quality rules and managing those rules as enterprise knowledge.

I provide a breakdown of the dimensions of data quality, and delineate a framework for expressing data quality rules, measuring those rules, and assessing levels of data quality in a "Data Quality Scorecard." This scorecard can then be used as a benchmark and basis for a continuous information quality improvement program.

In addition, we look at how understanding the business rules associated with the use of information throughout an enterprise can enhance the overall value of the enterprise knowledge asset. Integrating business rules in use across the organization is an important step in enhancing the enterprise knowledge resource, and we have found this to be a successful paradigm in knowledge management applications deployed with our customers.

Data quality problems are widespread, menacing, and can cause serious operational and strategic problems in any organization. By reading my book, I hope to expose some of the critical issues associated with poor data quality and to demonstrate that by fixing the root of data quality problems, organizations can reduce costs due to error detection, correction, and rework, and increase profits by making strategic use of high quality information.

I am a consultant in the area of knowledge management and data modeling, and I have read all the major books on the topic of data quality, and this book is, by far, the best treatement of the subject.

Enterprise Knowledge Management is a great handbook for both the manager and the practitioner - Loshin deals with the personal and political aspects of data ownership, buildingan ROI model for data cleansing, and a concise methodology about how to measure levels of data quality.

I have heard speeches by a handful of the major speakers in the area, and my impression is that they are willing to tell you to go and measure data quality, or to talk about data quality issues, but they would be hard-pressed to actually solve the problems. From reading this book, it is clear that Loshin is an expert in this area, and that he has not only dealt with the high level aspects of data management but also has experience in the trenches.

This book is perfect for both manager and technical people dealing with data warehousing or data migration projects.

As a data warehouse practitioner for over 12 years, I was recently challenged at my current employer to help assemble a global data quality team and process. Having done much of the work before on a piecemeal basis, we made steady progress.

When I received my copy of "Enterprise Knowledge Management," I found two important things:
1. We were definitely on the right track, and
2. There were some things we had missed.

David Loshin has put together an excellent field guide to all aspects of data quality. It is very easy to understand, and contains practical, effective suggestions. Most importantly, it is a true "soup to nuts" guide to data quality. There is very little that you might need to improve your company's "knowledge quotient" that you will not find here.

I have heartily recommended this book to a number of people when asked about data warehousing and data quality. You'll not find a better handbook anywhere.

At the time the book was published I worked as a data quality manager at the New Zealand Ministry of Health focusing on the implementation of the Data Quality Strategy for National Health Databases. It was a great help for us. We've implemented many of David Loshin's principles. Most importantly it helped us to understand that the majority of our DQ problems were not due to the poor data management processes, but because of the inadequate system's design or poor data model, which was either conceptually or contextually incorrect, incomplete or inaccurate.

Most of the literature on Data Quality focuses on the challenges of creating and maintaining a data warehouse. Thankfully, for those of us trying to improve the integrity of the information in our OLTP databases, this book presents a methodology which is not specific to any one data environment.

This book is packed with lists of cases to consider for each step of the methodology. Each case is nicely documented. Actually, much of the book is taken filled with the documentation for each case, which may cause a person to lose sight of the methodology that is being presented.

I am person who prefers to learn concepts. I am not as interested in memorizing details. Hence, I would read this book, skipping most of the documentation in the lists, instead focusing on understanding the methodology. Thereafter, I would use this book as a reference when needing information on a particular step of the methodology.

While I am not a technical person, this booked helped me enormously to understand the management issues that surround data quality. In today's world, I am shocked that more companies are not using this approach to save massive sums of money. This book has given me the tools to do so within my company. I highly recommend it!

David has written an excellent data quality book. He focuses on a real works around data quality. He presents a practical approaches how to solve a different types of quality defects and also pointed out main quality principles. But reader must think how to apply mentioned principles and approaches in reader's organization.

Simply, good reading with application on a real cases.

This book is NOT about enterprise management, it's about SQL. If I had wanted a book on SQL, I would have bought a book about SQL. I wanted a book on Enterprise Knowledge Management. This is not it.