‘Unknown or Unknowable’... Yet Shocking!

The everyday use of organizational data offers a staggering hidden opportunity

Published: Monday, December 19, 2016 - 18:30

Those of you familiar with W. Edwards Deming know that his Funnel Experiment ultimately shows that a process in control delivers the best results if left alone. Funnel Rule No. 4, also known as a “random walk”—i.e., making, doing, or building your next iteration based on the previous one—has been influencing Deming’s philosophy for the last 35 years. Isn’t it obvious that many times any resemblance between what one observes vs. Deming’s original intent is purely coincidental?

I’ve said it before: People don’t need statistics. They need to know how to solve their problems. All that’s needed are a few simple tools and a working knowledge of variation to be able to distinguish between common and special causes.

To illustrate, let’s return to last month’s column, where we considered a quarterly review meeting at which a department supervisor was charged with accounting for results vs. meeting arbitrary numerical goals.

An example of ‘unknown’ and ‘unknowable’

The estimable, late Lloyd S. Nelson, a true statistical giant, once said, “The most important figures that one needs for management are unknown or unknowable, but successful management must nevertheless take account of them.”

Here’s an I-chart of “red, yellow, green” tabulated data shown at the quarterly meeting:

Goal: 10 percent or less. If the people at the meeting had been open to a new (and correct) way of defining performance vs. a goal, they would have realized that the department supervisor had met her “tough” goal all year—because its average was 10 percent. And, rather than using the special cause strategies of 1) treating every individual cancellation or no show as a special cause; and 2) treating any deviation from the goal as a special cause (especially any individual month that isn’t green), they could have interpreted the chart for appropriate action: It’s all common cause.

Routine use of traffic-light charts unwittingly destroys organizations because it pretty much treats all common cause as special. How much organizational time is consumed by this?

During the quarterly meeting, the 0.6-percent alleged increase in the difference between the current year’s average of 10.0 percent and the previous year’s average of 9.4 came under scrutiny. But given that the plot of the 32 months of data indicated no special causes, there is no difference.

Then there’s the additional time-wasting nonsense of leaders making suggestions about what they believe to be the “low-hanging fruit,” which in last month’s scenario was used to hold the supervisor accountable for another, even “tougher” goal for the next year. 

Mark Graham Brown has estimated that 50 percent of the time executives spend in meetings involving data is waste, which leads to even more waste: people spending time looking for (and no doubt finding) and fixing the reasons why they didn’t meet some arbitrary numerical goal. 

What’s the objective of these meetings? Meet goals or improve processes? 

As Lao Tzu might say: If all of your focus is on meeting the goal, you will not meet the goal.

Frightened people are clever. They will waste a lot of time and energy doing their best to meet a goal by any means possible. It’s all part of the game of working on the number instead of the process. What do these games cost you? “Plot the dots” to see whether your interventions are working. As you will see, any goal has absolutely nothing to do with approaching how one would improve a process.

This is only one example demonstrating how the everyday, organizational use of data offers a staggering hidden opportunity. If you can’t think of at least a dozen similar examples in your work, take my advice from the last month’s column:
1. Think of Lao Tzu’s famous line: “The journey of 1,000 miles begins with a single step.”
2. Rewind and restart that journey, but this time take that first step: Just plot over time a number that makes your organization “sweat.”

Begin to apply some of what I’ve talked about to your chart and watch how the conversations and the ways you think about data change.

‘But Davis, what are these common cause strategies you’re talking about?’

Be patient. If you have truly rewound, you’re going to be amazed at what you’ll learn by just plotting a few critical numbers over time.

This raises some powerful questions you may have never considered:
• What data should be plotted?
• Where are the data?
• What do they mean?
• To whom?
• Who should see them?
• Why?
• Do they have a clear objective?
• How are they being used?
• Is there anything “wrong” with this data that should be fixed to use it properly? 

Regarding that last question:
• Does everyone calculate it exactly the same? (Probably not, and you’ll be surprised at the extent of it.)
• If it is a count of incidents, does everyone agree on the threshold that makes a situation go from a nonincident (= 0) to an incident (= 1)? (Probably not.)

Here’s a simple example: Is Pluto a planet (= 1) or not (= 0)? It doesn’t matter; just agree, or your data are contaminated!

Which way is best for your objective that will let you take the appropriate action?

Don’t these questions help to integrate and clarify aims and systems all at once? Unless you ask these questions, you may become a victim of (or even perform) an alleged statistical analysis I call PARC. Not sure what that means? You should. It’s the most commonly used statistical analysis. See my post, “Can You Prove Anything With Statistics?” A hearty laugh awaits you.

Oh, and how many statistical tools have I used so far? 

Next time, two important common cause strategies. 

Meanwhile, just “plot the dots,” please!

About The Author

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.