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Data Quality: The Accuracy Dimension is about assessing the quality of corporate data and improving its accuracy using the data profiling method. Corporate data is increasingly important as companies continue to find new ways to use it. Likewise, improving the accuracy of data in new information systems is fast becoming a major goal as companies realize how much it affects their bottom line. Data profiling is a new technology that supports and enhances the accuracy of databases throughout major IT shops. Jack Olson explains data profiling and shows how it fits into the larger picture of data quality.

FEATURES

• Provides an accessible, enjoyable introduction to the subject of data accuracy, peppered with real-world anecdotes.
• Provides a framework for data profiling with a discussion of analytical tools appropriate for assessing data accuracy.
• Is written by one of the original developers of data profiling technology.
• An informative reference for any data management staff, IT management staff, and CIOs of companies with data assets.

About the Author

Jack Olson has a B.S. degree from the Illinois Institute of Technology and an M.B.A. from Northwestern University. He has spent the last 36 years developing commercial software and is an expert in the field of data management systems and tools. His career includes several years at IBM, BMC Software, and Peregrine Systems. Currently he is chief technology officer and vice president of engineering at Evoke Software, where he created the concept of data profiling.

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