Hard Core Data Profiling

For almost two decades, experts have advocated data profiling as a means of enabling successful data warehousing. While many organizations have taken the directive seriously, few have realized the promised return on the investment of time and money. In many cases, this is because organizations have not defined what they expect to get out of the process or how to use the knowledge and information to be gained through data profiling. This session will discuss risks and success factors related to data profiling. It describes an approach that enables organizations to get more value from the process through better analysis and more usable metadata. Attendees will learn:

The pitfalls of profiling and how to avoid them
How to define goals for data profiling and a plan to achieve them
How to capture results of analysis in a consistent, consumable form
How to address findings in ways that improve overall data quality

Laura Sebastian-Coleman, Data Quality Center of Excellence Lead at Cigna, has worked on data quality in large health care analytic data warehouses since 2003. Cigna is a global health service company dedicated to helping people improve their health, well-being and sense of security. Laura has implemented data quality metrics and reporting, launched and facilitated data quality working groups, and contributed to data consumer training programs. She has led efforts to establish data standards and to manage metadata for large analytic data warehouses. In 2009, she led a group of analysts at Optum in developing the original Data Quality Assessment Framework (DQAF) which is the basis for her book "Measuring Data Quality for Ongoing Improvement" (Morgan Kaufmann, 2013). She is the DAMA Publications Director and is the 2015 recipient of the IAIDQ Distinguished Member Award.