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Davis Balestracci

Management

The One Rare Leadership Skill

Humility results from a basic understanding of variation​

Published: Wednesday, October 30, 2019 - 11:03

In 2006 I was at a presentation by a world leader in quality (WLQ) who has been singing W. Edwards Deming’s praises since the late 1980s and even does the famous red bead experiment as part of some of his plenaries.

He presented the following bar graph showing a comparison of the sum of rankings for 10 aspects of 21 counties in a small country’s healthcare system (considered on the cutting edge of quality). Lower sums are better: Minimum = 10, maximum = 210, average = 10 × 11 = 110.

He even mentioned something about “quartiles.”

My antennae went up. A bar graph? With absolutely no context of variation for interpretation? Quartiles? And a literal interpretation of the rankings? 

Envision a meeting to discuss these rankings, possibly revise them, and then decide on how to take action. We’ve all been at these types of meetings. I’m reminded of a favorite saying of Deming: “Off to the Milky Way!”

Let’s consider the process-oriented and systems-thinking approach Deming used in his red bead experiment.

I wrote the WLQ for the raw data, and he graciously complied.

I won’t bore you with the correct statistical analysis, but it did indeed strongly demonstrate the presence of differences. So, how does one go about finding them? By using an analog to that used by Deming in the red bead experiment, called analysis of means (ANOM):

Note that the points are not connected (there is no time order), and I chose the horizontal-axis order to go from smallest score to largest. “Three” standard deviation limits are used as common-cause limits (as in the red bead experiment comparing workers)—in this case, 110 ± 55 (55 to 165).

The statistical interpretation: 1) There are two counties’ performances “outside the system” (Deming’s nomenclature)—County No. 1 is superior, and County No. 21 subpar compared to their counterparts; 2) the other 19 counties are, based on these data, indistinguishable (i.e., “in the system”).

Also, there is neither a top quartile nor a bottom quartile.

In The New Economics (MIT Press, second edition 2000), Deming shows a similar chart and comments about the performance equivalent of counties 2 to 20: These cannot be ranked! 

The purpose of ANOM is to expose any hidden special-cause variation in existing components of a common system. Dialogue then ensues about how to reduce any inappropriate and unintended variation (County 21) while investigating whether there are possible lessons to be learned by studying beneficial variation (County 1), which could improve the quality of the entire system.

A potential transformation opportunity... unrecognized

When I shared this analysis with the WLQ, I was shocked at his lack of comprehension. Our verbatim email correspondence follows.

World leader of quality: “A subtle issue you did not tackle is the political-managerial issue of communicating such insights to [the two special cause counties] and the counties that thought they were ‘different,’ but, statistically, aren’t. I wonder what framework one could use to approach that psychological challenge.”

Davis: “As I say to my audiences, ‘Hey... I’m just the statistician, man.’

“I’m going to be very hard on you here, but I think the issue is how people and leaders like you are going to facilitate these difficult conversations... which will be profoundly different... and productive! This is the leadership that quality gurus keep alluding to... and seems to be in short supply.

My job is to keep you all out of the‘data swamp; however, I would be a very willing participant. I have a saying, ‘I’m the statistician; I know nothing. You’re the [leaders]; you know too much. That makes us a great team!’

“And I would love to pilot some of these types of analyses with you or other leaders—we need to figure out what this process should be. This is potentially very exciting and could quantum-leap the quality improvement movement.

“My point is that this ‘language’ needs to be a fundamental piece of any improvement process... and led by leaders who understand it and are now promoted into positions of leadership only if they understand it. If this could become culturally inculcated, then the ongoing daily defensiveness reacting to data stops... period!

“The discussion will then focus, as it should, on process.

“I am seeing far too much concern about ‘hurting people’s feelings.’ This would change that as well as promote conversations leading to appropriate action.

“That’ s what I’ve been saying the last few years—we need new conversations... and this could be a key catalyst.”


WLQ: “Nope. I don’t buy it. Yes, I am a leader and need to carry the message. But I know you too well to let you off the hook. I’d love to see you try to lead these conversations and experiment with approaches. You’re a leader, too.”

Davis: “Give me an opportunity, and I will do my best to lead that conversation (and feel that we could begin by co-facilitating it). Have you fathomed the potential of this?

That last email has never been answered. At his previous insistence, I sent the analysis with explanation to the original executive group that collected and summarized the data. No reply.

Here it is, more than 10 years later, and several follow-up gentle email reminders have been ignored (including a recent conversation two weeks ago). I’ve given up any hope of participating in what would be a truly groundbreaking educational opportunity. And I’ve had no more luck persuading any other leader to give it a try.

I often feel like I’m dealing with the caricature of an old-time, blowhard politician personified by the Warner Brothers’ classic cartoon character Foghorn Leghorn: “Go, I say, go away, boy, you bother me.”

This example’s statistical simplicity is what Deming demonstrated in his seminars (and, once again, the red bead experiment is an ANOM). After 30+ years of trying to teach similar things with real data, I am still amazed at the walls of fierce resistance put up by leaders due to clueless ignorance and arrogance.

How can any leader in good conscience abdicate the responsibility to comprehend the liberating, transformational power of a simple understanding of variation—at the cost of tolerating the confusion, conflict, complexity, and chaos caused by countless Ouija-board data meetings?

Is that too much to ask of someone making a huge salary whose actions affect the “five-figure salary” folks?

Bottom line: Many people (even those who do the red bead experiment) who think they “get” Deming’s message, quite frankly, don’t.

The all-too-easy takeaway from the red bead experiment is to embrace its observers as victims and patronize them with the overly simplistic, “You need ‘joy in work!’ Just do it!” platitude. That is only a secondary, tangential lesson. The real lesson has been lost: The need for an even deeper understanding of variation to create leadership humility that will, in turn, create the joy.

For more insights in lessening the crazy-making going on with frontline doctors, and even more important than this column’s example, read this column of mine from June 2017, and this follow-up from July 2017.

How many leaders are willing to own and deal with the lack of “joy in work!” caused by confusion, conflict, complexity, and chaos (and for some, their tantrums) due to lack of a basic knowledge of variation in “the everyday use of data” process?

This remained the underlying source of Deming’ s unforgiving curmudgeonliness until the end of his life.

Discuss

About The Author

Davis Balestracci’s picture

Davis Balestracci

Davis Balestracci is a past chair of ASQ’s statistics division. He has synthesized W. Edwards Deming’s philosophy as Deming intended—as an approach to leadership—in the second edition of Data Sanity (Medical Group Management Association, 2015), with a foreword by Donald Berwick, M.D. Shipped free or as an ebook, Data Sanity offers a new way of thinking using a common organizational language based in process and understanding variation (data sanity), applied to everyday data and management. It also integrates Balestracci’s 20 years of studying organizational psychology into an “improvement as built in” approach as opposed to most current “quality as bolt-on” programs. Balestracci would love to wake up your conferences with his dynamic style and entertaining insights into the places where process, statistics, organizational culture, and quality meet.