PM4: Data Quality Assessment and Measurement

Experts agree that to improve data quality, you must be able to measure data quality. But determining what and how to measure is often challenging. The purpose of this tutorial is to provide participants with a comprehensive and adaptable approach to data quality assessment.

The challenges of measuring data quality and how to address them.
DQ assessment in context: Understand the goals and measurement activities and deliverables associated with initial assessment, in-line measurement and control, and periodic reassessment of data. Review a template for capturing results of data analysis from these processes.
Defining DQ requirements: Learn how to define measurable characteristics of data and establish requirements for data quality. Review a template designed to solicit and document clear expectations related to specific dimensions of quality.
Using measurement for improvement: Share examples of measurements that contribute to the ongoing improvement of 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.