Editorial Review:
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
Cached date: AWS Called=true
You may also be interested in these products:
These categories may also be of interest to you:
Customer Reviews
Average Customer Rating: 
Misleading  2008-10-06
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.
Excellent practise book in data quality  2006-05-29
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.
David Loshin's book and quality improvement of New Zealand National Health Information 2005-09-21
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.
Its all in the Details 2003-09-14
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.
Data Quality in the Real World 2003-02-05
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.
|
|
