The various improvement approaches are, in essence, all pretty much the same. Any competent practitioner would neither want to be called a “guru” nor have any problems dealing with another competent practitioner of another improvement philosophy.

In my opinion, any approach should also involve the use of data in some way, shape, or form. I once had a lean sensei (local “guru”?) vehemently make the point that lean does not involve data at all. I'll let you decide. Would you rather be effective or right?

Unfortunately, data conjure up the dreaded word “statistics”... and a huge misconception. The influence of W. Edwards Deming has created the mistaken belief that mass training in statistics will cure all quality ills. We all know the monster that has created.

Not ‘statistics’ but ‘variation’

I believe that one of Deming’s most profound statements was: “If I had to reduce my message to management to just a few words, I’d say it all has to do with reducing variation.” Reduced variation ultimately yields a more predictable process. But let me first expand your conception of “variation.”

Twenty-five years ago, I learned a wonderfully simple model summarizing the four stages of a change process, whether personal or organizational.

• Awareness
• Breakthrough in knowledge
• Choosing a breakthrough in thinking
• Demonstrating a consistent breakthrough in behavior

Here’s the point: Unless thinking changes, behavior will not change—long term.

Changing behavior, as most of us know deep down, is hard work. In my research on personal and cultural behavior change, it takes a visceral, very uncomfortable wrestling with one’s current belief system—ingrained, unconscious axioms developed in reaction to life experiences up to age 20. It then takes the stark realization (awareness) that these beliefs are perfectly designed to produce the results one is currently experiencing, both personally and professionally, and that have probably served one well... up until now.

As many of you know, I hate bar graphs. They are ubiquitous, and most of them are worthless. I'll make maybe two exceptions: 1) a Pareto analysis; 2) a comparative set of stratified histograms disaggregating a stable period of performance (a Pareto analysis proxy for continuous data). Displaying the latter as bars is one option; another—and my preferred—is displaying run or control charts on the same page and same scale.

About 15 years ago, I picked up my morning paper and saw an article, with the accompanying graph below (see my last column), rating the 20 health systems in my metropolitan community on the question, “Would you recommend your clinic to adult friends or family members?” I happened to work at Clinic 19 (maybe not the best service, but excellent care).

Click here for larger image.

As improvement professionals, part of our learning curve is the experience of facilitating project teams that fail miserably. Then, despite the necessary lessons learned, there still remain some very real dangers lurking in any project, but it goes beyond organizing and facilitating a team. What about the choice of project?

In the post mortem—if indeed there even is a post mortem—the question that inevitably comes up for projects that didn’t even get close to desired results is, “Why was this project chosen in the first place?” With a collective shoulder shrug, the consensus many times seems to be, “It seemed like a good idea at the time.”

Here are five project evaluation criteria by Matthew E. May. He suggests scanning the current organizational project portfolio and evaluating your role by giving each project a star rating: one star for each criterion. Ask yourself, “What percentage of my work is five-star projects?”