A Whole-Brain Look at the Challenges of Measurement and Motivation
Note: This is Part 4 in a four-part series on the challenges to being data-driven. To understand the purpose of this series and read from the beginning, Click Here.
In Part 1, Part 2 and Part 3 of this article, we covered three of the four spectrums of the Whole-Brain model applied to data-driven decision-making. I discussed the challenges in choosing what to measure, how to measure it, and, more importantly, how to interpret the metric.
Now we have reached the conclusion of this series, which covers the ultimate goal in data-driven decision-making. How can we change motivations and behaviors through data? How can we affect the “who” or Red part of the Whole-Brain model to achieve our collective goals?
The Who — Tying Measurement to Motivation
The holy grail of data-driven decision-making has nothing to do with the data itself; it has to do with the behaviors we want to change. Tracking the stock price in and of itself is meaningless; it’s motivating employees to take actions that will help increase the price that we care about. It’s the increased demand in stock purchases from investors that we seek.
We worked through the previous three steps — deciding what to metrics to track, how to track them, and interpreting our results, all in the effort to motivate and incentivize behaviors. In the same way that complex interactions make it difficult to measure outcomes, and in the same way our brains make it difficult to interpret metrics, our humanity makes it difficult to drive desired behaviors even when we get everything else right.
Take this example from “The Success Equation” by Michael J. Mauboussin. For 20+ years, stock options have been used as an incentive to align leadership’s actions to the increase in a company’s shareholder value, which should, in turn, increase their individual compensation. There is a problem with this approach. A stock price involves considerable randomness due to complex interactions of many factors, meaning the actions of leadership don’t always have a direct correlation with price movement. If the correlation is low, then stock options cease being a good way to motivate leadership actions.
There is a way to fix this problem, according to Mauboussin. Company boards can index the exercise price of options to a market index based on a basket of peer group stocks, which would remove a good deal of the randomness associated with macroeconomic factors or industry-specific changes. That way, the movement of a stock price would be more correlated with specific actions of leadership. This isn’t the typical approach, but it would create better alignment.¹
In Part 3 of this article, we looked at how our brain wiring makes it difficult to interpret the results of our data collection rationally. The same battle between emotions and intellect in our brains makes it difficult to produce the right motivations and behaviors; only the challenges are even greater. Here is a look at the multitude of headwinds when trying to tie measurement to motivation.
Rationality Quotient
In “The Success Equation,” Mauboussin describes the work of Keith Stanovich, Emeritus Professor of Applied Psychology and Human Development at the University of Toronto. Stanovich distinguishes between IQ (Intelligence Quotient), something we’re all familiar with, and RQ (Rationality Quotient). He argues these are distinct abilities, and RQ is more important when observing cognitive skills. Stanovich identifies the key attributes to RQ:
- Adaptive behavioral acts
- Judicious decision-making
- Efficient behavioral regulation
- Sensible goal prioritization
- Reflectivity
- Proper calibration of evidence
According to Stanovich, many people have intelligence but lack the ability to think and behave rationally. Some might call this “book smart” versus “street smart.” The difference between IQ and RQ is due to our mental processing and the limits to what we know. When problem-solving and deciding on behaviors, our natural tendency is to use tools with low cognitive concentration rather than using the tools in our brain that are slow, deliberative and mentally intensive. Stanovich calls this the “Cognitive Meizer,” which contributes to an ego-centric point of view and the penchant for biases that lead to irrationality. As a result, one may have a very high IQ but make mistakes that seem obvious from a rational standpoint.²
We all know someone like this. Sometimes the most intelligent people can be the hardest to motivate because they often have large egos, which can contribute to a larger gap between IQ and RQ.
Political Theater
Ahh, politics, the tangled web that exists in every workplace, waiting to catch the unsuspecting employee. One of my first bosses told me something I’ll never forget — he said managers have power, but leaders have influence. Unfortunately, there are far more managers than leaders, and the hierarchical structure within organizations allows these managers to play politics with their power. The most common manifestation of this problem is the continuation of bad ideas, despite what the metrics say.
I recently listened to a podcast where the host spoke about what caused the Space Shuttle Challenger disaster in 1987, which exploded when a joint in its right solid rocket booster (SRB) failed at liftoff. The engineers knew the O-ring seals holding the joint together had not been tested at the low temperatures on the day of the launch — in other words, the data was clear about the risk. But NASA management decided to move forward anyway, and the result was a catastrophe.
This is an unfortunate example where behavior was influenced more by the power wielded by managers than a rational interpretation of data. I imagine we’ve all been in situations when politics got in the way of good decision-making. Data is supposed to reduce these types of situations, but power will try to trump the numbers when they don’t align.
Prospect Theory
In 2002, Daniel Kahneman won the Nobel Prize in Economics for his work with Amos Tversky in 1979 for creating Prospect Theory, which is the idea that behavior is at odds with economic theory. Their work formed the basis of behavioral economics, giving us insight into why people make decisions that don’t follow rational economic thought.
Sometimes people don’t make rational economic decisions, which makes it very difficult to use metrics to create economically incentivize behaviors. In “The Success Equation,” Mauboussin describes a study on employment compensation, where researches asked subjects who would be happier — a person making $36K in a firm where the average person made $40K or making $34K where the average person made $30K. Eighty percent of the respondents selected the employee making $34K!³
In the world of technology, the entire open source movement flies in the face of basic economic theory. Why would someone want to build software with no compensation when they can build the exact same thing within a company for a salary? The Cathedral and the Bazaar, by Eric S. Raymond, gives a fascinating look at the answer. While there are many elements to the open source movement, one aspect of his book struck with me — programmers are often incentivized by status or recognition more than money.
Because we’re human, our emotional complexity means we can’t always rely on economic incentives to drive our motivations. The key is understanding what drives people and working to align incentives with those drivers, which might not always be economic in nature.
Anchoring
Here’s a great anecdote from Nassim Nicholas Taleb in “Fooled by Randomness.” Say you’re an investor, and you get a windfall profit of $1 million. Then the next month you lose $300K. Alternatively, say you simply earned a profit of $700K. Which scenario would feel worse? The former would hurt emotionally, even though the net financial result between the scenarios is the same.
This emotion occurs because of anchoring — what Taleb calls a “dependence on local rather than global context that can impact our perception of well-being.” People tend to forecast a result based on a number they have in mind. Kahneman and Tversky showed an example of this by asking subjects to estimate the proportion of African countries in the UN after making them pull a random number between 0 and 100. People guessed in relation to that arbitrary number, which acted as an anchor.⁴
Price negotiation is grounded in the idea of anchoring — that’s why a seller usually doesn’t want to present the first number because it becomes the anchor of the negotiation. If a seller proposes a price too low, they leave money on the table. If they propose a price too high, they might jeopardize the sale; so it’s better to let the buyer set the anchor and react accordingly.
When deciding how to act based on the interpretation of KPIs, ask yourself whether you have any implicit anchors driving your behavior. If so, remove those anchors and look at the data objectively, then ask yourself whether you would make the same decisions.
Ego and Self-Awareness
Good decision-making requires us to take an honest look at our behaviors based on the data in front of us, and sometimes that involves admitting we were wrong so we can change course. Eric Ries, the author of “The Lean Startup,” says we need to “pivot or persevere” when conducting the learning step of the Build, Measure, Learn cycle. How many of us are good at pivoting when we’re wrong?
Not many, unfortunately, and it is because our ego gets in the way. The Oxford dictionary defines ego as “a person’s sense of self-esteem or self-importance.” It is a natural human behavior to want to be right. Wait But Why blogger Tim Urban would say this is because of our Primitive Mind’s need for safety and belonging. Although we think of self-esteem as an individual trait — how one views themselves — it’s really how we view ourselves in the context of others (yet another form of anchoring). Admitting we’re wrong tells others we fell short, and that is a tough thing for some people to do.
The business world is littered with tales of peopling failing to pivot because of their egos, despite what the metrics foretell. A few months ago, I read an article on CNBC about Elon Musk pitching an executive of the Yellow Pages in Canada back in 1995. He and his brother Kimbal started a company to create an online business directory, and they attempted to partner with the Yellow Pages to put their content online. The executive took a large Yellow Pages book and threw it down at the Musks, saying, “You ever think you’re going to replace this?” We all know how that story ended.⁵
Granted, in 1995, there weren’t that many people who knew about the internet, so perhaps we can forgive this executive for letting his ego get in the way. But what about Kodak? They invented the digital camera in 1975, and despite producing a report predicting future trends toward digital in the market, management decided that change was unnecessary. The company had a full ten years to act once the market began to shift, but they stayed the course, and by the time of their bankruptcy in 2012 they had lost 75% of their value.⁶
I suspect that in decision-making, the level of a person’s ego is directly proportional to that person’s reputation. We cannot underestimate the degree to which one’s motivations are driven by a desire to manage their reputation more than a desire to take necessary risks to improve metrics. Mauboussin makes this point clearly in “The Success Equation” by citing a passage John Maynard Keynes wrote in 1936 — “it is better for reputation to fail conventionally than to succeed unconventionally.” This incentivizes people to be unconventional enough to get an edge but not so much that they’ll get fired because of organizational influence.⁷
In “Fooled By Randomness,” Taleb lauds George Soros as an example of someone who removes ego from decision-making. The famous activist investor’s strength, according to Taleb, is that he revises opinions rapidly without the slightest hint of embarrassment. If the dynamics of a particular investment suddenly change, he doesn’t have a problem saying he was wrong. The lesson from Soros is “to start every day convinced we’re all mistake-prone idiots but happened to be endowed with the privilege of knowing it.”⁹
Feedback and Rewards
In the book “Hooked,” by Nir Eyal, behavior change is driven by a feedback loop and variable reward systems. I know this might come as a surprise to you, Gen Zers, but Gen Xers like myself use to enjoy visiting the mailbox everyday just as assuredly as you enjoy your Instagram feed, and for the same reason. Each time we visited, we got a variable reward — we never knew what we might find (check or bills).
The key to this is the feedback loop. In the case of stickiness within social media, the loop is fairly simple — show relevant photos from friends and influencers to please the visual senses. But in the case of motivating behavior change based on measurements, we have to be careful of what Mauboussin calls the “illusion of feedback.”
The illusion of feedback refers to the idea that feedback provided after one outcome causes the change in the next outcome, rather than the causality being related to something else. Mauboussin gives an example I’m familiar with — imagine a child comes home with a bad grade. If you’re like me, you might limit technology and other extra-curricular activities and force the child to study more. Now, say they do better on the next test — it would be easy to believe my feedback and remediation caused the improvement. But instead, the more likely cause is simply that the child has reverted to the mean because, on average, outlying scores on the high and low end will tend back toward the middle.⁹
To ensure the appropriate feedback loop, the key is to include the proper rewards with high-quality feedback. Mauboussin describes a process called Deliberate Practice as a method to support high-quality feedback — the idea of working on a task that is just beyond our abilities, responding to the feedback of our failures.¹⁰ At my firm, Pariveda Solutions, we approach employee professional development by placing people on the “learning edge” through the use of Deliberate Reflective Ongoing Practices (DROPs), Career Development Plans (CDPs) and tools like Kazoo to create feedback loops.
Creating the right motivations means getting our rewards right based on the metrics, then getting the feedback loop right, and finally making sure the feedback is contributing to the behavior changes we’re seeing. It is a delicate balancing act, but when done correctly and based on sound interpretation of metrics, it can be a powerful thing.
Conclusion
I’ve spent a lot of time in this four-part series sharing with you how hard it is to be data-driven, but my goal with this series isn’t to discourage you from trying. Quite the opposite — I want your data-driven journey to be as successful as possible, and I hope this article helps you navigate the pitfalls.
Ever since “Freakonomics,” there has been a certain pizzazz and coolness about identifying unique insights through data. As our datasets grow in size and complexity, we will inevitably become more data-driven. But it is incumbent on us to use this data to enhance the value of our humanity, while not letting our humanity restrict the value of the data.
Best of luck in your data-driven endeavors!
Bibliography
- The Success Equation, Michael J. Mauboussin — Chapter 11
- The Success Equation, Michael J. Mauboussin — Chapter 5
- The Success Equation, Michael J. Mauboussin — Chapter 8
- Fooled by Randomness, Nassim Nicholas Taleb — Chapter 11
- https://www.cnbc.com/2020/04/03/elon-musk-pitched-the-head-of-yellow-pages-before-the-internet-boom.html
- http://www.bridgepointgroup.com.au/7-spectacular-business-strategy-failures/
- The Success Equation, Michael J. Mauboussin — Chapter 8
- Fooled by Randomness, Nassim Nicholas Taleb — Chapter 13
- 1. The Success Equation, Michael J. Mauboussin — Chapter 10
- 2. The Success Equation, Michael J. Mauboussin — Chapter 11
