There is a penalty for ignorance. We are paying through the
-- Dr. Deming
6- to 7-minute read over a break or lunch, but: be prepared for some "AHA!"s that will reinforce the need for you to do what you already deeply know inside must be done.
"Adopt the New Philosophy" (Deming Point 2) and "Institute Leadership" (Point 7) to STOP Unwitting Destruction*
According to Mark Graham Brown (from his book Keeping Score
), 50 percent of time leaders spend in meetings involving data is waste, 80 percent of the pounds (kilos) of published financial data is waste, 60 percent of the pounds (kilos) of published operational data is waste, and middle management wastes one hour a day
poring over these reports. Do you have any idea what this costs
– but does it even matter?
In meetings involving leaders and middle managers, there seems to be a code of virtually universal management laws of using operational data. This code has its own vocabulary
emphasizing the words "tough," "stretch," "low-hanging fruit," "accountability," and the phrase "I don't like these numbers" as well as the tantrum phrases such as "Find out what happened here!", "What are you going to do about it?!", "I want a plan to fix this!"
Nine Universal Leadership/Management Laws of Data Display and Use*
* Perfectly designed to create Confusion, Conflict, Complexity, and Chaos...and increase Costs
Does something magical happen in January (or at the beginning of any fiscal year)? What have you observed so far this
January 2nd, most of us come to work at the same organization that existed on December 31st. But:
1. As of January 1, the past no longer exists except as yearly averages, with two appropriate exceptions:
(a) The current year-to-date figure can be compared only with the exact same time span of the previous year
Assuming seasonality and treating any differences as special causes
(b) Using only this same month's performance 12 months ago to compare with the current month.
- Trap: Assuming each month is a special cause and treating the two months' year-over-year difference as a special cause
2. If at all possible, the data should be
presented in tabular form on which we will draw little circles around any numbers we don't like.
(a) (Optional): Put "Why?!" or "What happened?!" in red ink next to the circle and mail it to the appropriate person.
- Trap: Human variation in perception of and response to variation. treating it all as special cause – plus unnecessary time spent arguing over whose circles are
most important and/or coming to consensus about which are vitally important for action now. (aka MBLC: Management By Little Circles)
Balestracci's Profound Law of Numbers: Given a set of numbers, one will be the largest, one will be the smallest, 10 percent will be the top (and bottom) 10 percent, and 25 percent will be the top (and bottom) quartiles – and the biggest difference between any two months
will be the biggest difference since the previous biggest difference.
I wonder whether a better explanation of MBLC might be: Management By Literal Chaos (or Confusion or Complexity or Conflict)?
Where would you put your circles?
3. For important data, we may need an occasional graph of no more than the last 24 months
(a) Use one or more of the three following displays:
- Separate yearly averages superimposed on the running 24-month record and trend line(s) added
- Plotted year-over-year by month in a "copulating earthworm plot" to compare differences and look for seasonality (the only exception for possibly using more than 2 years), or
- As year-to-date side-by-side monthly bar graphs of each of the two months' performances.
(b) More preferable and less confusing is the past 12 months (only) of data to see how we're doing – as bar
graphs with a trend line (always).
- Trap: More human variation in reaction to such nonsensical displays. I didn't make up this following figure of a number that made people sweat:
What are you supposed to DO with this?
4. When displaying financial data, use rolling averages whenever possible.
- Trap: Common cause data can exhibit strong evidence of very obvious special causes
that don't exist! To demonstrate, here are time plots of the exact same data:
- Top plot: randomly generated data that has no special causes;
- Middle plot: its rolling averages of 4 (e.g. analogous to commonly used 4-quarter rolling average);
- Bottom plot: its rolling averages of 12 (e.g. equivalent to common 12-month rolling average, often used in
calculating "days outstanding accounts receivable").
Yes...the exact same data!
5. The difference between this month's performance and last month's performance might need to be explained, especially if trending in the wrong direction by "too
- Trap: Once again treating the difference and perceived trend as a special cause due to human variations in perceptions of the exhibited variation and how large it "should" be.
6. The performances of this month, last month, and same month 12 months ago give an idea of the overall trend and may need a trend line so we can compare it with last month's trend. Then we can update our projection of year end
- Trap: Once again treating common cause as special cause and, additionally, a 33 percent risk of calling three data points a trend – either all going up or all down – when it isn't.
7. When at all possible, convert a table of numbers to its traffic light equivalents.
Entities being compared Columns: Consecutive weekly performance
Any green indicator is fine. We will discuss whom should get recognition; but, more preferably, one of these optional tougher strategies should be considered to get even better results:
Use a reward process to stretch them further: For example, if they get [pick a number] greens-in-a-row, we will tell them "Send out for pizza and send us the bill"...and then stretch their green endpoint.
(b) Set a standard: No more than [pick a number] months in-a-row can be non-green and will require a special report. [Pick a number]-reds-in-a-row will require a face-to-face meeting with management to present a
plan for better performance."
(c) For very important numbers, show them we mean business! (e.g., customer satisfaction survey results): go around as a leadership group weekly or monthly and plant a red, yellow, or green flag in each department based on their most recent result [DB: TRUE – I didn't make this one up!]
- Trap: High risk of treating common
cause as special...and destroying cultural morale (and any remaining respect for the leaders)
8. Current month and year-to-date performances are compared to goals and recorded as variances.
(a) (Optional: getting tough): The [arbitrary percentage] of people having the largest variances will need to write a special report about what they're going to do about it and present their
results to us next month.
- Trap 1: Treating all variances as special cause and choosing an arbitrary percentage of people as a cutoff for needing explanations – assuming they are special causes when many are probably not.
- Trap 2: Time wasted with people preparing these nonsense reports and unnecessarily presenting them at the next meeting.
- Trap 3: At this subsequent
meeting once again allowing human variation on perceived variation to demand questionable actions based on these reports, especially asking, "Where is your low-hanging fruit?"
9. All goals must end with a zero or a 5 with one exception: for what we know to be an impossible situation, we will ask for only a 3 percent stretch.
- Trap: Using goals to motivate. Treating individual differences between
current performances and their goals as special causes. No use of an IChart to gain knowledge of process's actual performance compared to any goal to suggest that maybe a common cause strategy is required.
What about the tiresome annual ritual of the budget?
only do all of these laws apply to the everyday use of routine data, but did you realize they all come into play during the entire budgeting process?
- Trap: Treating every year as a special cause in spending...and taking up to 30 percent of peoples' time with creating, adjusting, re-adjusting (and re-adjusting), and routine cost-cutting meetings throughout the year.
Aren't you perfectly designed to spend what you spend? How about plotting spending as a starting point and then using some common cause strategies?
Isn't all this nonsense merely reacting to the data's reflection of routine daily common causes of confusion, conflict, complexity, and chaos as if they were special causes – "tampering"?
Isn't all this just a symptom of a much, much deeper systemic (additional common cause) problem – data
"Unknown or Unknowable?" Perhaps, but who needs figures to see this widespread organizational cancer as a staggering cost?
Might data INsanity and its toxic consequences be the root cause of Dr. Deming's disgust with American management?
Don't believe me? There's only one way to find out: Plot some dots!
To be continued next
examples of real leadership situational data in Chapter 2 of Data Sanity suggest
alternatives for YOU to do – initially behind your own closed doors.
It's not a matter of if, but when you will make a huge impact on an organizational "big
Data Sanity: A Quantum Leap to Unprecedented Results is a unique synthesis of the sane use of data, culture change, and leadership principles to create a road map for excellence.
One of its major goal is to create a common
organizational language for healthier dialogue about reducing ongoing confusion, conflict, complexity, and chaos.
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