Have you ever been responsible for a data collection where any resemblance between what you designed and what you got back was purely coincidental? When that happens, yet again, I say to myself, “Well, it was perfectly clear to me what I meant.”

Consider the use of statistics as a data process, or rather, four processes: measurement, collection, analysis, and interpretation. Each of these has six sources of process inputs: people, methods, machines, materials, measurements, and environments. Each also has an inherent “quality” associated with it and is subject to the influences of outside variation to compromise this quality. Unless the variation in these processes is minimized, there is a danger of reacting to the variation in the data process instead of the process you are trying to understand and improve.

What is the biggest danger? Human variation, which includes our perception of the variation (“measurement”) and our executing the “methods” of measurement, collection, analysis, and interpretation.

In that context, let’s consider each of these four data processes:

As you all know, the influence of W. Edwards Deming on my career and thinking has been profound. A criticism always leveled at him was that he was short on specifics—but he would always growl at someone who alluded to this, “Examples without theory teach nothing!”

Enter Joseph Juran, the other quality giant of the 20th century. When I worked at 3M during the 1980s, they had several sets of his 16-video Juran on Quality Improvement series. I studied it hard and watched several tapes many, many times. He had a good empirical sense (and sense of humor), and having been around the block once or twice, a lot of wisdom. So, let’s apply some of that wisdom to the two scenarios from part one of this three-part series.

Juran always advised as a first strategy: “Exhaust in-house data.” An initial chart analyzing your process is important before doing this. Here’s a key principle of common cause analysis that almost everyone overlooks: The data from any common cause period on a control chart can be aggregated to attempt a “stratification” and apply Juran’s beloved Pareto Principle.

“If Japan Can… Why Can’t We?” was an American television episode that aired on June 24, 1980, broadcast by NBC as part of its show, NBC White Paper. That episode is often credited with beginning the quality revolution and introducing the methods of W. Edwards Deming to American managers.

In the mid-1970s, bachelor’s degree in chemical engineering in hand, I remember my initial eye-opening experience of being exposed to applied statistical methods. I realized that, as an engineer, how could I not use them? It inspired me to get a master’s degree in statistics and become a member of one of the ubiquitous internal statistical consulting groups for major corporations that existed at that time—groups that have, sadly, long since disappeared. As a relatively new and very excited industrial master of science statistician, I was consistently mystified by the fierce resistance I encountered to the obvious need to use applied statistics to improve the quality of manufacturing products and the research in their development.

“I’m shocked... shocked to find that gambling is going on in here!”

—Casablanca’s Captain Renault, as he’s closing down Rick’s Cafe... while being handed his gambling winnings

I saw an abstract of a recent talk by several “experts” who have been very active selling (expensive) improvement initiatives during the last 5–10 years. They do this via lots of training, tools, “sharing best practices,” and exhorting people with, “If they can do it, you can do it.” Meanwhile they are creating a massive subculture of qualicrats.