Data Quality: The Accuracy Dimension / Edition 1 available in Paperback
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 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.
• 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.
• Is a must-read for any data management staff, IT management staff, and CIOs of companies with data assets.
|Series:||Morgan Kaufmann Series in Data Management Systems Series|
|Edition description:||New Edition|
|Product dimensions:||0.66(w) x 6.00(h) x 9.00(d)|
About the Author
Jack E. Olson is a widely recognized database technology expert. His career includes significant contributions at IBM, BMC, Evoke, and now NEON Enterprise Software, where he serves as Chief Technology Office. Olson is author of Data Quality: The Accuracy Dimension, also published by Morgan Kaufmann. The inventor of record on several patents, he holds a BS from the Illinois Institute of Technology and an MBA from Northwestern University.
Table of Contents
Preface. Acknowledgements. Forward. Part 1: Understanding Data Accuracy: The Data Quality Problem. Definition of Accurate Data. Sources of Inaccurate Data. Part 2: Implementing a Data Quality Assurance Program: Data Quality Assurance. Data Quality Issues Management. The Business Case for Accurate Data. Part 3: Data Profiling Technology: Data Profiling Overview. Column Property Analysis. Structure Analysis. Single Data Rule Analysis. Complex Object Data Rule Analysis. Value Rule Analysis. Summary. Appendixes : Example of Column Properties, Data Structure, Data Rules and Value Rules. Content of Data Profiling Repository. Bibliography. Index.