Synopses & Reviews
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.
- 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.
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.
* 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
ailable 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.
President, Knowledge Integrity Incorporated, Silver Springs, MD, USA
Table of Contents
Chapter 1 - Introduction
Chapter 2 - Who Owns Information?
Chapter 3 - Data Quality in Practice
Chapter 4 - Economic Framework of Data Quality and the Value Proposition
Chapter 5 - Dimensions of Data Quality
Chapter 6 - Statistical Process Control and the Improvement Cycle
Chapter 7 - Domains, Mappings, and Enterprise Reference Data
Chapter 8 - Data Quality Assertions and Business Rules
Chapter 9 - Measurement and Current State Assessment
Chapter 10 - Data Quality Requirements
Chapter 11 - Metadata, Guidelines, and Policy
Chapter 12 - Rule-Based Data Quality
Chapter 13 - Metadata and Rule Discovery
Chapter 14 - Data Cleansing
Chapter 15 - Root Cause Analysis and Supplier Management
Chapter 16 - Data Enrichment/Enhancement
Chapter 17 - Data Quality and Business Rules in Practice
Chapter 18 - Building the Data Quality Practice