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Data Quality: The Accuracy Dimension (The Morgan Kaufmann Series in Data Management Systems)

ISBN: 1558608915
EAN: 2147483647

Year: 2003
Pages: 133

Authors: Jack E. Olson

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• Table of Contents
• BackCover
• Data Quality-The Accuracy Dimension
• Foreword
• Preface
• Part I: Understanding Data Accuracy
• Chapter 1: The Data Quality Problem
• 1.1 Data Is a Precious Resource
• 1.2 Impact of Continuous Evolution of Information Systems
• 1.3 Acceptance of Inaccurate Data
• 1.4 The Blame for Poor-Quality Data
• 1.5 Awareness Levels
• 1.6 Impact of Poor-Quality Data
• 1.7 Requirements for Making Improvements
• 1.8 Expected Value Returned for Quality Program
• 1.9 Data Quality Assurance Technology
• 1.10 Closing Remarks
• Chapter 2: Definition of Accurate Data
• 2.2 Principle of Unintended Uses
• 2.3 Data Accuracy Defined
• 2.4 Distribution of Inaccurate Data
• 2.5 Can Total Accuracy Be Achieved?
• 2.6 Finding Inaccurate Values
• 2.7 How Important Is It to Get Close?
• 2.8 Closing Remarks
• Chapter 3: Sources of Inaccurate Data
• 3.2 Data Accuracy Decay
• 3.3 Moving and Restructuring Data
• 3.4 Using Data
• 3.5 Scope of Problems
• 3.6 Closing Remarks
• Part II: Implementing a Data Quality Assurance Program
• Chapter 4: Data Quality Assurance
• 4.1 Goals of a Data Quality Assurance Program
• 4.2 Structure of a Data Quality Assurance Program
• 4.3 Closing Remarks
• Chapter 5: Data Quality Issues Management
• 5.1 Turning Facts into Issues
• 5.2 Assessing Impact
• 5.3 Investigating Causes
• 5.4 Developing Remedies
• 5.5 Implementing Remedies
• 5.6 Post-implementation Monitoring
• 5.7 Closing Remarks
• Chapter 6: The Business Case for Accurate Data
• 6.1 The Value of Accurate Data
• 6.2 Costs Associated with Achieving Accurate Data
• 6.3 Building the Business Case
• 6.4 Closing Remarks
• Part III: Data Profiling Technology
• Chapter 7: Data Profiling Overview
• 7.1 Goals of Data Profiling
• 7.2 General Model
• 7.3 Data Profiling Methodology
• 7.4 Analytical Methods Used in Data Profiling
• 7.5 When Should Data Profiling Be Done?
• 7.6 Closing Remarks
• Chapter 8: Column Property Analysis
• 8.2 The Process for Profiling Columns
• 8.3 Profiling Properties for Columns
• 8.4 Mapping with Other Columns
• 8.5 Value-Level Remedies
• 8.6 Closing Remarks
• Chapter 9: Structure Analysis
• 9.1 Definitions
• 9.2 Understanding the Structures Being Profiled
• 9.3 The Process for Structure Analysis
• 9.4 The Rules for Structure
• 9.5 Mapping with Other Structures
• 9.6 Structure-Level Remedies
• 9.7 Closing Remarks
• Chapter 10: Simple Data Rule Analysis
• 10.1 Definitions
• 10.2 The Process for Analyzing Simple Data Rules
• 10.3 Profiling Rules for Single Business Objects
• 10.4 Mapping with Other Applications
• 10.5 Simple Data Rule Remedies
• 10.6 Closing Remarks
• Chapter 11: Complex Data Rule Analysis
• 11.2 The Process for Profiling Complex Data Rules
• 11.3 Profiling Complex Data Rules
• 11.4 Mapping with Other Applications
• 11.5 Multiple-Object Data Rule Remedies
• 11.6 Closing Remarks
• Chapter 12: Value Rule Analysis
• 12.1 Definitions
• 12.2 Process for Value Rule Analysis
• 12.3 Types of Value Rules
• 12.4 Remedies for Value Rule Violations
• 12.5 Closing Remarks
• Chapter 13: Summary
• 13.2 Moving to a Position of High Data Quality Requires an Explicit Effort
• 13.3 Data Accuracy Is the Cornerstone for Data Quality Assurance
• Appendix A: Examples of Column Properties, Data Structure, Data Rules, and Value Rules
• A.2 Tables
• A.3 Column Properties
• A.4 Structure Rules
• A.5 Simple Data Rules
• A.6 Complex Data Rules
• A.7 Value Rules
• Appendix B: Content of a Data Profiling Repository
• B.2 Business Objects
• B.3 Domains
• B.4 Data Source
• B.5 Table Definitions
• B.6 Synonyms
• B.7 Data Rules
• B.8 Value Rules
• B.9 Issues
• References
• Books on Data Quality Technologies
• Articles
• Index
• Index_B
• Index_C
• Index_D
• Index_E
• Index_F
• Index_G-H
• Index_I
• Index_K-L
• Index_M
• Index_N
• Index_O
• Index_P
• Index_Q-R
• Index_S
• Index_T
• Index_U
• Index_V
• Index_W-Z
• List of Figures