Strategy. Innovation. Brand.
Corporate Culture: Counting The Uncountable
You can’t measure love.
I was in a meeting not long ago with a client whose organization is undergoing a significant transformation. We were discussing what needed to change and how we might promote the appropriate change efforts. A senior executive spoke up to say, “Well, you get what you measure.” Nobody challenged the assumption behind the thought and we began to focus on how to measure change in the organization.
I had, of course, heard similar statements many times before. Business schools emphasize measurement as a key ingredient of management. As a leader, you point the way, establish some key measurements, and then harvest the results. Sounds simple, doesn’t it?
But think about the things that we don’t bother to measure – or that we don’t know how to measure. These include love, respect, hope, initiative, creativity, open-mindedness, ability to resolve conflicts, receptiveness to new ideas, focus, drive, and resilience. Do we really not care about these things?
We tell managers that the most important thing they can do is build a positive, engaging organizational culture. (See here and here). We also tell them they can only get what they measure. Yet many of the components of culture are simply not measureable. I have yet to hear a manager say, “In the third quarter, we increased corporate resilience by 3.2% compared with the same quarter in the previous year.”
So, how do we help managers build a positive culture even when they can’t measure it? Here are some thoughts:
- The answer is not a number on a piece of paper. It’s OK to look at numbers but we need to remember that a number is an abstraction of a measurement that is an abstraction of reality. Looking only at numbers is like driving your car by looking only at the speedometer.
- Trust is an unrecognized but important ingredient. We focus on measurement partially because we don’t trust our eyes, and ears, and subordinates. A measurement is supposed to give us an “objective” view of the organization. But a measurement is only as objective as the person taking the measurement – which is to say, not very. If we can create a culture built on trust, we can learn more about our organization than a measurement will ever tell us. (In this regard, it’s useful to review Edwards Deming’s 14 points of management and his red bead experiment).
- Learning to observe is more important than learning to measure. We forget that measurement is just one form of observation. Management by walking around still works. Taking groups of employees out to dinner will teach you more than a spreadsheet will. Practice being naïve – ask questions that a rookie would ask. You may think you don’t need to ask certain questions because you already know the answer. But you’re probably wrong.
- A good conversation is more important than a staff meeting. Staff meetings can be useful but they’re also very structured. You get the information that the structure permits. A good conversation is much more open-ended. You can wander about until you touch on the really important issues.
- Questions are more important than answers. A number from a measurement purports to give us an answer. But it only answers the question you asked – and the answer may not be accurate. Learn how to ask probing questions that get rich and unexpected answers. Tip: a good course on critical thinking will help.
I think we obsess about measurement because we have a bad case of physics envy. We want our organization to behave like a physics experiment. If we apply Force X, we get Result Y. It doesn’t work that way and never will. Time to get over the measurement mania.
Is Critical Thinking The Ultimate Job Skill?
Interesting syllogism. But the premise is unsound. That’s why I get the big bucks.
When I went off to college, my mother told me, “Now remember … you’re going to college to learn how to think. Don’t miss that lesson.”
I wonder what she would say if she were sending me off to college today. It might be more along the lines of, “Now remember … you’re going to college to get a good job. Don’t blow it.”
We can only judge programs and processes based on their goals. If the goal of government is to provide good services at a reasonable cost, we might give it a fairly low grade. However, if the goal of government is to increase employment, then we might evaluate it more positively. The same is true of higher education. So what is the goal of higher education? Is it to teach students how to think? Or is it to provide them skills to get a job?
I would argue that the goal of higher education – indeed of any education – is to improve the students’ ability to think. Good thinking can certainly help you get a job. In fact, it may be the ultimate job skill. But good thinking can take you much farther than a good job. Here’s my thinking on the issue:
1) Thinking is foundational — the essence of running a business (or a government) is to make decisions about the future. To make effective decisions, we need to understand how we think, how our thinking can be biased, and how to evaluate evidence and arguments. If we know everything about finance, for instance, but don’t know how to think effectively, we will make decisions based on faulty evidence, weak arguments, and unconscious biases. By chance, we might still make some good decisions. But we should remember Louis Pasteur’s thought, “Chance favors the prepared mind.”
2) The future is unknowable — our niece, Amelia, will graduate from college next May. If she works until she’s 65, she’ll retire in the year 2060. What skills will employers need in 2060? Who knows? As I reflect on my own education, the content I learned in college is largely useless today. The processes I learned, however, are still very relevant. Thinking is the ultimate process. Amelia will still need to think effectively in 2060.
3) Thinking promotes freedom – if we can’t think for ourselves, we will forever be buffeted by other people’s agendas, desires, ambitions, and rhetorical excesses. Critical thinking allows us to assess ideas and social movements and make effective decisions on our own. We can frame our thinking as we wish and not allow others to create frames for us. We can identify the truth rather than relying on others to tell us what is true. Critical thinking, allows us to take control of our own destiny, which is the essence of freedom. I don’t know of any other discipline that can make the same claim.
Innovation In Our Refrigerator
How can I innovate here?
When we think of innovation, we often think of bright young people working in creatively organized offices while pushing the envelope and thinking outside the box. It’s fun, exciting, challenging, and maybe even a little bit sexy. It’s the kind of job we all want.
But what about the rest of the world?
Much of the innovation that I have observed takes place in rather mundane places and involves rather ordinary business or social processes. It’s the act of taking some thing (or some process), observing how it’s used, and designing a better way to do it. If we think about innovation only as the process of creating something entirely new, we’ll miss many, many opportunities to change the world for the better.
Take our refrigerator, for instance.
Suellen and I were recently on vacation and asked a very responsible young woman named Alyssa to house sit for us. As soon as we left, our refrigerator stopped working. Alyssa organized a service call, coordinated with the repairman, and had the refrigerator repaired in a jiffy. From our perspective, it was virtually painless (except for the bill, of course).
When we arrived back home, we also got a pleasant surprise. Alyssa had completely reorganized the interior space of the refrigerator. She had examined the food items we keep and adjusted shelves and drawers to fit our lifestyle. She used the space much more efficiently and made frequently used items more readily available. It’s now simpler and easier to store and retrieve our food.
Why hadn’t we organized our fridge more effectively? We never thought about it. It’s one of those ordinary, mundane appliances that doesn’t attract our attention. It’s not leading edge, or state of the art, or sexy. Though we use it every day, we never considered how we might improve it. When the refrigerator arrived in our house, we simply put our food in it. We didn’t think about rearranging shelves or drawers to improve utility and efficiency. It took Alyssa to apply design thinking to an ordinary, everyday item.
We describe some things as “wallpaper” because they recede into the background. We don’t need to pay much attention to them. We don’t consider them as opportunities to create and innovate. But we interact with our wallpaper everyday. That makes even small innovations meaningful and impactful. If you want to be an innovator, spend more time on wallpaper and less time thinking outside the box.
Questions, Proxies, and Health
When faced with a difficult question, we often substitute a simpler question and answer that instead. Here are three examples:
- What’s the crime rate in your neighborhood? – We probably don’t keep close tabs on the crime rate. We’re not naturally good at statistics either so it’s difficult to develop a reasonable estimate. So we substitute a different question: How easy is it for me to remember a crime committed in my neighborhood? If it’s easy to remember, we guess that the neighborhood has a high crime rate. If it’s hard to remember, we guess a lower rate. (I’ve adapted this example from Daniel Kahneman’s book, Thinking Fast and Slow).
- How’s your car running? – It’s hard to know how well a car is running in this age of sophisticated electronics. So we answer a different question: How does the car sound? If it’s not making strange noises – knocks and pings – it must be running well and performing optimally.
- How effective is your shampoo? – I suppose we could study our hair’s health every day but most of us don’t. So we answer a simpler question: How much lather does your shampoo produce? If we get a lot of lather, the shampoo must be effective.
How many do I need to get 10 calories of energy?
In each case, we substitute a proxy for the original question. We assume that the proxy measures the same thing that the original question aimed to measure. Sometimes we’re right; sometimes we’re wrong. Most often, we don’t think about the fact that we’re using a proxy. System 1 does the thinking for us. But we can, in fact, bring the proxy to System 2 and evaluate whether it’s effective or not. If we think about it, we can use System 2 to spot errors in System 1. But we have to think about it.
As it happens, System 1 uses proxies in some situations that we might never think about. Here’s an example: How much food should you eat?
(The following is based on a study from the University of Sydney. The research article is here. Less technical summaries are here and here).
We tend to think of food in terms of quantity. System 1 also considers food as a source of energy. System 1 is trying to answer two questions: 1) How much energy does my body need? 2) How much food does that translate to?
Our bodies have learned that sweet food delivers more energy than non-sweet food and can use this to translate from energy needs to food requirements. Let’s say that the equation looks something like this:
1 calorie* of energy is generated by 10 grams of sweet food
Let’s also assume that our body has determined that we need 10 calories of energy. A simple calculation indicates that we need to eat 100 grams of sweet food. Once we’ve eaten 100 grams, System 1 can issue a directive to stop eating.
Now let’s change the scenario by introducing artificial sweeteners that add sweetness without adding many calories. The new translation table might look like this:
1 calorie of energy is generated by 30 grams of artificially sweetened food
If we still need 10 calories of energy, we will need to eat 300 grams of artificially sweetened food. System 1 issues a directive to stop only after we’ve eaten the requisite amount.
System 1 can’t tell the difference between artificially and naturally sweetened foods. It has only one translation table. If we eat a lot of artificially sweetened food, System 1 will learn the new translation table. If we then switch back to naturally sweetened foods, System 1 will still use the new translation table. It will still tell us to eat 300 grams of food to get 10 calories of energy.
We would never know that our brain makes energy/quantity assumptions if not for studies like this one. It’s not intuitively obvious that we need to invoke System 2 to examine the relationship between artificial sweeteners and food intake. But like crime rates or cars or shampoos, we often answer different questions than we think we’re answering. To think more clearly, we need to examine our proxies more carefully.
*It’s actually a kilocalorie of energy but we Americans refer to it as a calorie.
Male Chauvinist Machines
Yanks win last night?
Do men and women think differently? If they do, who should develop artificial intelligence? As we develop AI, should we target “feminine” intelligence or “masculine” intelligence? Do we have enough imagination to create a non-gendered intelligence? What would that look like?
First of all, do the genders think differently? According to Scientific American, our brains are wired differently. As you know, our brains have two hemispheres. Male brains have more connections within each hemisphere as compared to female brains. By contrast, female brains have more connections between hemispheres.
Men, on average, are better at connecting the front of the brain with the back of the brain while women are better at connecting left and right hemispheres. How do these differences influence our behavior? According to the article, “…male brains may be optimized for motor skills, and female brains may be optimized for combining analytical and intuitive thinking.”
Women and men also have different proportions of white and gray matter in their brains. (Click here). Gray matter is “…primarily associated with processing and cognition…” while white matter handles connectivity. The two genders are the same (on average) in general intelligence, so the differences in the gray/white mix suggest that there are two different ways to get to the same result. (Click here). Women seem to do better at integrating information and with language skills in general. Men seem to do better with “local processing” tasks like mathematics.
Do differences in function drive the difference in structure or vice-versa? Hard to tell. Men have a higher percentage of white matter and also have somewhat larger brains compared to women. Perhaps men need more white matter to make connections over longer distances in their larger brains. Women have smaller heads and may need less white matter to make the necessary connections — just like a smaller house would need less electrical wire to connect everything. Thus, a larger proportion of the female brain can be given over to gray matter.
So men and women think differently. That’s not such a surprise. As we look ahead to artificial intelligence, which model should we choose? Should we emphasize language skills, similar to the female brain? Or local processing skills, similar to the male brain? Should we emphasize processing power or information integration?
Perhaps we could do both, but I wonder how realistic that is. I try to imagine what it would be like to think as a woman but I find it difficult to wrap my head around the concept. As a feminist might say, I just don’t get it. I have to imagine that a woman trying to think like a man would encounter similar difficulties.
Perhaps the best way to develop AI would involve mixed teams of men and women. Each gender could contribute what it does best. But that’s not what’s happening today. As Jack Clark points out, “Artificial Intelligence Has A “Sea of Dudes’ Problem”. Clark is mainly writing about data sets, which developers use to teach machines about the world. If men choose all the data sets, the resulting artificial intelligence will be biased in the same ways that men are. Yet male developers of AI outnumber females by a margin of about eight-to-one. Without more women, we run the risk of creating male chauvinist machines. I can just hear my women friends saying, “Oh my God, no!”
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