Title: Data: Guilty Until Proven Innocent
Accurate, reliable data are essential when making business decisions, however the quality of data is only as good as the system or process used during data collection; hence, it’s best to always assume data are guilty until proven innocent to avoid making erroneous decisions that can have costly business results. Measurement system analysis (MSA) techniques evaluate the accuracy and precision of the system’s data outputs, but sometimes standard techniques can’t be used. To provides a way to determine when measurement processes are inadequate so they can be improved, this presentation discusses specific alternative MSA methods to document, audit, and identify problems in measurement processes. The benefits of using these methods include reduced frustration for practitioners about how to ensure data quality and integrity when standard MSA techniques are not applicable, and improved decision making regarding what specific corrective actions are actually needed to improve process performance.
Jamison V. Kovach is the PMI Houston Endowed Professor in Project Management at the University of Houston. She received her Ph.D. in Industrial Engineering from Clemson University. Her industrial experience includes more than five years as a product and process improvement engineer in the U.S. textile industry. Her current research investigates the use of methods for product and process innovation, expanding the use of these methods, and developing new improvement approaches. For her work, Dr. Kovach was recognized as the 2010 ASQ Feigenbaum Medalist, and she received the ASQ Six Sigma Forum Award for the Advancement of Six Sigma in 2019. In addition, Dr. Kovach is an Academician in the International Academy for Quality, an ASQ Fellow, and the Editor for Lean & Six Sigma Review.