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Data quality. The accuracy dimension
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List of Figures
Chapter 1: The Data Quality Problem
Figure 1.1: Examples of cross-company systems.
Figure 1.2: Demands on operational databases.
Figure 1.3: Reasons not much has been done about quality problems.
Chapter 2: Definition of Accurate Data
Figure 2.1: Breakdown of data within a set of data.
Figure 2.2: Chart of accurate/inaccurate values and those that are findable and fixable.
Figure 2.3: Effects of improvements.
Figure 2.4: Step function influence on tolerance levels.
Chapter 3: Sources of Inaccurate Data
Figure 3.1: Areas where inaccuracies occur.
Figure 3.2: Accuracy of decayable elements over time.
Figure 3.3: List of projects that require restructuring and movement of data.
Figure 3.4: Steps in the data movement process.
Chapter 4: Data Quality Assurance
Figure 4.1: Components of a data quality assurance group.
Figure 4.2: Components of a data quality assurance program.
Figure 4.3: Methodology comparisons.
Chapter 5: Data Quality Issues Management
Figure 5.1: Issue management phases.
Figure 5.2: Factors in evaluating data capture processes in the data capture environment.
Figure 5.3: Data quality issue remedy types.
Chapter 6: The Business Case for Accurate Data
Figure 6.1: General model of business case.
Figure 6.2: Project selection criteria.
Figure 6.3: The business case for project services.
Chapter 7: Data Profiling Overview
Figure 7.1: Data profiling model.
Figure 7.2: Data profiling steps.
Figure 7.3: Example of a business object.
Chapter 8: Column Property Analysis
Figure 8.1: Definitional elements.
Figure 8.2: Typical properties.
Figure 8.3: Example of domain versus property definitions.
Figure 8.4: Column property analysis process.
Figure 8.5: Typical data types.
Figure 8.6: List of valid value rule types.
Chapter 9: Structure Analysis
Figure 9.1: Functional dependencies.
Figure 9.2: Keys.
Figure 9.3: Example of data in normal forms.
Figure 9.4: Examples of denormalized tables.
Figure 9.5: Synonym types.
Figure 9.6: Structure analysis process.
Figure 9.7: Multiple-column synonym example.
Figure 9.8: Synonym classifications.
Chapter 10: Simple Data Rule Analysis
Figure 10.1: Process for analyzing simple data rules.
Chapter 11: Complex Data Rule Analysis
Figure 11.1: Process for profiling complex data rules.
Chapter 12: Value Rule Analysis
Figure 12.1: Value rule analysis process
Appendix A: Examples of Column Properties, Data Structure, Data Rules, and Value Rules
Figure A.I: Table diagram.
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
List of Figures
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