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Today, companies capture and store tremendous amounts of information about every aspect of their business: their customers, partners, vendors, markets, and more. But with the rise in the quantity of information has come a corresponding decrease in its quality--a problem businesses recognize and are working feverishly to solve.
Enterprise Knowledge Management: The Data Quality Approach presents an easily adaptable methodology for defining, measuring, and improving data quality. Author David Loshin begins by presenting an economic framework for understanding the value of data quality, then proceeds to outline data quality rules and domain-and mapping-based approaches to consolidating enterprise knowledge. Written for both a managerial and a technical audience, this book will be indispensable to the growing number of companies committed to wresting every possible advantage from their vast stores of business information.
Key Features
* Expert advice from a highly successful data quality consultant
* The only book on data quality offering the business acumen to appeal to managers and the technical expertise to appeal to IT professionals
* Details the high costs of bad data and the options available to companies that want to transform mere data into true enterprise knowledge
* Presents conceptual and practical information complementing companies' interest in data warehousing, data mining, and knowledge discovery
- Sales Rank: #2095201 in Books
- Published on: 2001-01-31
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 1.13" w x 7.52" l, 1.93 pounds
- Binding: Paperback
- 493 pages
From the Back Cover
Your company captures and stores tremendous amounts of information about every aspect of its business. But with this rise in the quantity of information has come a corresponding decrease in its quality. Now more than ever, reversing this trend may spell the difference between success and failure. How can you and your organization respond to this challenge?
Enterprise Knowledge Management gives you just what you need: a precise yet adaptable methodology for defining, measuring, and improving data quality and managing business intelligence. This one-of-a-kind book begins by laying out an economic framework for understanding the real business value of data quality. It then outlines rules for measuring data quality and determining where it can and should be improved. Finally, it teaches proven techniques through which you can achieve meaningful advances in the quality of your business data, including domain- and mapping-based consolidation of enterprise knowledge.
Features
- Expert advice from a highly successful data quality consultant.
- Rigorously methodical in its approach to the problem and the detailed solution it presents.
- Teaches you to measure quality in real business terms and to achieve meaningful, demonstrable improvement.
- Uniquely combines business acumen and technical expertise-an indispensable resource for managers and IT professionals alike.
- Documents the high costs of bad data and details the options available to any company that wants to transform mere data into true enterprise knowledge.
About the Author
David Loshin is President of Knowledge Integrity, Inc., a company specializing in data management consulting. The author of numerous books on performance computing and data management, including “Master Data Management" (2008) and “Business Intelligence - The Savvy Manager’s Guide" (2003), and creator of courses and tutorials on all facets of data management best practices, David is often looked to for thought leadership in the information management industry.
Most helpful customer reviews
20 of 22 people found the following review helpful.
Excellent Methodology!
By Data Quality
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.
7 of 8 people found the following review helpful.
Its all in the Details
By R. Williams
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.
11 of 14 people found the following review helpful.
Data Quality in the Real World
By Glenn Rutz
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.
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