Book description
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
Table of contents
- Titlepage
- Contents
- PREFACE
- Chapter 1: INTRODUCTION
- Chapter 2: WHO OWNS INFORMATION?
-
Chapter 3: DATA QUALITY IN PRACTICE
- 3.1 Data Quality Defined: Fitness for Use
- 3.2 The Data Quality Improvement Program
- 3.3 Data Quality and Operations
- 3.4 Data Quality and Databases
- 3.5 Data Quality and the Data Warehouse
- 3.6 Data Mining
- 3.7 Data Quality and Electronic Data Interchange
- 3.8 Data Quality and the World Wide Web
- 3.9 Summary
-
Chapter 4: ECONOMIC FRAMEWORK OF DATA QUALITY AND THE VALUE PROPOSITION
- 4.1 Evidence of Economic Impact
- 4.2 Data Flows and Information Chains
- 4.3 Examples of Information Chains
- 4.4 Impacts
- 4.5 Economic Measures
- 4.6 Impact Domains
- 4.7 Operational Impacts
- 4.8 Tactical and Strategic Impacts
- 4.9 Putting It All Together — The Data Quality Scorecard
- 4.10 Adjusting the Model for Solution Costs
- 4.11 Example
- 4.12 Summary
- 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
- 8.1 Data Quality Assertions
- 8.2 Data Quality Assertions as Business Rules
- 8.3 The Nine Classes of Data Quality Rules
- 8.4 Null Value Rules
- 8.5 Value Manipulation Operators and Functions
- 8.6 Value Rules
- 8.7 Domain Membership Rules
- 8.8 Domain Mappings and Relations on Finite Defined Domains
- 8.9 Relation Rules
- 8.10 Table, Cross-Table, and Cross-Message Assertions
- 8.11 In-Process Rules
- 8.12 Operational Rules
- 8.13 Other Rules
- 8.14 Rule Management, Compilation, and Validation
- 8.15 Rule Ordering
- 8.16 Summary
-
Chapter 9: MEASUREMENT AND CURRENT STATE ASSESSMENT
- 9.1 Identify Each Data Customer
- 9.2 Mapping the Information Chain
- 9.3 Choose Locations in the Information Chain
- 9.4 Choose a Subset of the DQ Dimensions
- 9.5 Identify Sentinel Rules
- 9.6 Measuring Data Quality
- 9.7 Measuring Data Quality of Data Models
- 9.8 Measuring Data Quality of Data Values
- 9.9 Measuring Data Quality of Data Domains
- 9.10 Measuring Data Quality of Data Presentation
- 9.11 Measuring Data Quality of Information Policy
- 9.12 Static vs Dynamic Measurement
- 9.13 Compiling Results
- 9.14 Summary
- Chapter 10: DATA QUALITY REQUIREMENTS
- Chapter 11: METADATA, GUIDELINES, AND POLICY
-
Chapter 12: RULE-BASED DATA QUALITY
- 12.1 Rule Basics
- 12.2 What Is a Business Rule?
- 12.3 Data Quality Rules Are Business Rules (and Vice Versa)
- 12.4 What Is a Rule-Based System?
- 12.5 Advantages of the Rule-Based Approach
- 12.6 Integrating a Rule-Based System
- 12.7 Rule Execution
- 12.8 Deduction vs Goal-Orientation
- 12.9 Evaluation of a Rules System
- 12.10 Limitations of the Rule-based Approach
- 12.11 Rule-Based Data Quality
- 12.12 Summary
- Chapter 13: METADATA AND RULE DISCOVERY
- Chapter 14: DATA CLEANSING
- Chapter 15: ROOT CAUSE ANALYSIS AND SUPPLIER MANAGEMENT
-
Chapter 16: DATA ENRICHMENT/ENHANCEMENT
- 16.1 What Is Data Enhancement?
- 16.2 Examples of Data Enhancement
- 16.3 Enhancement Through Standardization
- 16.4 Enhancement Through Provenance
- 16.5 Enhancement Through Context
- 16.6 Enhancement Through Data Merging
- 16.7 Data Matching, Merging, and Record Linkage
- 16.8 Large-Scale Data Aggregation and Linkage
- 16.9 Improving Linkage with Approximate Matching
- 16.10 Enhancement Through Inference
- 16.11 Data Quality Rules for Enhancement
- 16.12 Business Rules for Enhancement
- 16.13 Summary
- Chapter 17: DATA QUALITY AND BUSINESS RULES IN PRACTICE
-
Chapter 18: BUILDING THE DATA QUALITY PRACTICE
- 18.1 Step 1: Recognize the Problem
- 18.2 Step 2: Management Support and the Data Ownership Policy
- 18.3 Step 3: Spread the Word
- 18.4 Step 4: Mapping the Information Chain
- 18.5 Step 5: Data Quality Scorecard
- 18.6 Step 6: Current State Assessment
- 18.7 Step 7: Requirements Assessment
- 18.8 Step 8: Choose a Project
- 18.9 Step 9: Build Your Team
- 18.10 Step 10: Build Your Arsenal
- 18.11 Step 11: Metadata Model
- 18.12 Step 12: Define Data Quality Rules
- 18.13 Step 13: Archaeology/Data Mining
- 18.14 Step 14: Manage Your Suppliers
- 18.15 Step 15: Execute the Improvement
- 18.16 Step 16: Measure Improvement
- 18.17 Step 17: Build on Each Success
- 18.18 Conclusion
- INDEX
- BIBLIOGRAPHY
Product information
- Title: Enterprise Knowledge Management
- Author(s):
- Release date: January 2001
- Publisher(s): Morgan Kaufmann
- ISBN: 9780080505732
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