CIDA’s Compass – A Brief History of Continuous Improvement and Data Analytics
Last week we published our first post in the Continuous Improvement & Data Analytics (CIDA) Continuous Insights blog. In that introductory post we provided some background on the CIDA team, its foundation at BU, and its mission. This week we are following up with a brief discussion of the pairing of continuous improvement (CI) with data analytics (DA).
Continuous improvement methods have a history that goes back to the Industrial Revolution. The idea of monitoring a process, measuring outputs, and using these measurements to maintain standards of quality evolved side-by-side with the manufacturing and production methods that arose in the late nineteenth century. This application of a scientific approach to management grew rapidly around the world in the years after World War Two. Nowadays, continuous improvement concepts and techniques are a core component of most business education programs.
Concurrent with the development of CI was the development of gathering, summarizing, and analyzing those measurements. Anyone who has ever been bedeviled by an introductory statistics class has encountered formulas for the standard deviation or the student’s t-test. What many students might not have learned is that these approaches to data were only introduced in the late 19th and early 20th centuries and were developed to improve the quality of industrial processes.
The student’s t-test was invented in 1907 by an employee of the Guinness brewery (William Sealy Gossett) as a means of minimizing the amount of grain needed for quality control in the brewing process. This data analytics technique was created to solve a pressing problem, and to improve the processes of the organization.
Neither continuous improvement nor data analytics exist in a vacuum. Collecting accurate, verifiable data and analyzing it in a robust, efficient, and relevant way is a resource intensive process. Organizations need to expend these resources on efforts that improve processes, not just create impressive looking reports and dashboards.
Correspondingly, continuous improvement cannot be based on hunches or intuition. Measurement processes need to be verifiable and replicable to be of any real value to an organization.
In short, data analytics with no context is a waste of resources, and continuous improvement that isn’t based in data analytics is no more than guesswork.
Our goal at CIDA is to utilize the combined advantages of continuous improvement and data analytics in a collaborative team of specialist. On the CI side, consultants analyze and document processes to identify areas for improvement or increased efficiency. The DA side bring their expertise in data development and analysis to assist departments with KPIs and other important metrics.
Please continue to check back here at the CIDA website to learn more about continuous improvement, data analytics, and the projects we are undertaking.