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About the Presentation
This presentation provides guidance to organizations considering or preparing for data quality initiatives. We will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality can be engineered provides a useful framework in which to develop an organizational approach. This in turn will allow organizations to more quickly identify data problems caused by structural issues versus practice-oriented defects. Participants will also learn the importance of practicing data quality engineering quantification.
- What is data quality and why is it important?
- Data quality concepts & activities
- Data Quality Management (DQM) cycle
- Data quality awareness & requirements
- Data quality dimensions
- Case Study: Data Quality Golden Rules
- Data quality tools & frameworks
- Guiding principles & best practices
About the Speaker
Peter Aiken is an award-winning, internationally recognized thought leader in the area of organizational data management, architecture, and engineering. As a practicing data manager, consultant, author and researcher, he has been actively performing and studying these areas for more than 30 years. His sixth book is titled XML in Data Management. He has held leadership positions with the US Department of Defense and consulted with more than 50 organizations in 20 different counties. Dr. Aiken’s achievements have resulted in recognition in Outstanding Intellectuals of the 21st Century and bibliographic entries in Who’s Who of Emerging Leaders in America, Who’s Who in Science and Engineering, and other recognitions. His entertaining but clear and concise insights make him a sought after speaker, lecturer and consultant. He is an Associate Professor in Virginia Commonwealth University’s Information Systems Department and the Founding Director of datablueprint.com.
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