Enterprise Knowledge Management

  • ID: 3025321
  • Book
  • 493 Pages
  • Elsevier Science and Technology
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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
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Preface
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
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Loshin, David
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.
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