Data Collection Strategy

By: Jack B. ReVelle, Ph.D.

(This article is provided courtesy of the author Jack B. ReVelle, Ph.D.)

 

“Without data, you’re just another person with an opinion.” “In God we trust, all others bring data.” And so on. We collect data to help us to make better decisions than we would do otherwise, both individually and collectively. Better decisions come from a reduction in uncertainty, i.e., decisions based on guesswork, “gut feel,” or common sense. Data are facts, the performance measures or metrics that someone has collected; however, they are not information ready to be used in making decisions.

For data to become information ready for use by decision-makers, they must lead to understanding. To correct a problem we need to understand its nature and its causes. As data are collected and compared with desired performance levels, we are able to learn more about the causes or sources of a problem, what should be measured, and how it should be measured. This suggests a strategy for data collection.

The following is a partial checklist of steps that should be a part of the Data Collection Strategy employed within an organization:

  • Determine the purpose of the data to be collected. Will it be used to assess the status of a process or a product? Will it provide a basis for decisions regarding process or product quality?
  • Determine the nature of the data to be collected. Is it measurable (variable or continuous) data, or is it counted (attribute or discrete) data?
  • Determine the characteristics of the data to be collected. Can the data be easily understood by persons who will evaluate product and process improvement (including customers)?
  • Determine if the data can be expressed in terms that invite comparisons with similar processes. Can the performance metric/measure be expressed as ppm (parts per million), dpmo (defects per million defect opportunities), Cp or Cpk (process capability indices), or 6σ (Six Sigma)?
  • Determine if the data place priority on the most important quality influences and if the data are economical and easy to collect.
  • Determine the best type of data gathering check sheet to use: checklists, tally sheets, or defect density/concentration maps/diagrams.
  • Determine if it will be possible to use some form of random sampling or whether it will be necessary to use 100 percent data collection.

 

About Jack B. ReVelle, Ph.D.:

Dr. Jack B. ReVelle provides his advice and assistance as a consulting statistician.  His professional credentials include over 40 clients as well as over 100 books, handbooks, videos and software packages. Dr. ReVelle received his B.S. in Chemical Engineering from Purdue University and both his M.S. and Ph.D. in Industrial Engineering and Management from Oklahoma State University.  Prior to earning his Ph.D., he served 12 years in the U.S. Air Force.  During that time, he was promoted to the rank of major and was awarded the Bronze Star Medal while stationed in the Republic of Vietnam as well as the Joint Service Commendation Medal for his work in quality assurance with the Pentagon-based Defense Atomic Support Agency (DASA).  He has been selected as a Fellow by the American Society for Quality, the Institute of Industrial and Systems Engineers and the Institute for the Advancement of Engineering. In 2006 Dr. ReVelle was awarded the Oklahoma State University Lohmann Medal in recognition of his contributions to the art and science of quality assurance. In 2012 Dr. ReVelle was awarded the ASQ Shainin Medal for the development and application of creative and unique statistical approaches in the solving of problems relative to the quality of a product and service. In 2015 he was awarded the ASQ-Los Angeles Simon Collier Quality Award and this year he was inducted into the Oklahoma State University College of Engineering, Architecture, and Technology Hall of Fame for his lifetime of dedication to quality and statistics.