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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.
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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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