Data-Driven Change: Essential Mindsets

Data-driven cultures are obsessed with an open-minded pursuit of the truth, supported by rigorous analysis to drive faster and better decisions. Leaders frequently assume it works the other way:  that engaging in rigorous analysis will automatically lead to this open-minded pursuit of the truth. Instead, human nature often gets in the way.

Research from McKinsey revealed that only 28 percent of executives said the quality of strategic decisions in their companies was generally good. The root cause of these poor results was primarily managers struggling with cognitive biases such as overconfidence, confirmation bias, or groupthink. When the team examined what led to superior decisions, the quality of the decision process mattered more than that of analysis by a factor of six.

The quality of a decision process is determined by the degree to which you can objectively evaluate the evidence before you, explore alternative hypotheses, and engage in open debate. Analysis, however sophisticated and tech-enabled, is often used to reinforce what we believe rather than seek the truth. Cognitive biases hardwired into our brains can lead us to make poor decisions in life and work.

The use of mindsets can bring more objectivity to decisions by forcing you to reframe or rethink your approach to decision-making. Mindsets can provide a fresh perspective and take what psychologist and Nobel Prize laureate Daniel Kahneman calls the “outside view.”

By mindsets, we mean our state of mind when faced with a decision. They are a way of reframing the problem and seeing the situation through a different lens or perspective.  However, simply understanding mindsets and how they work is not enough. You need to use them.

Clear-eyed analysis often competes with bureaucracy, agendas, egos, and risk-averse, consensus-driven cultures. Mindsets help you take an outside view — outside yourself and your specific circumstances. Leadership and frontline staff can apply this thinking whether making big strategic bets or unit-level investments and operational improvements.

Mindsets, if applied systematically, help reinforce the rigor of analysis and establish consistent behaviors that translate into daily habits. They have two categories: foundational and transformational:

Foundational Mindsets

include self-awareness and a growth mindset. These are foundational because they,
1 – Help combat our natural tendency towards self-deception that cognitive biases reinforce, and
2 – Support the notion that we can change and improve ourselves through concerted effort and a systematic process.

Transformational Mindsets

include “think-like” and mental models. These move you closer to mindset mastery, where you can easily reframe and rethink situations using multiple lenses to see problems more clearly.

Understanding your role in the decision-making process and the additional context of the task at hand will help you assemble the best set of mindsets for each decision.

Self-awareness

As humans, we are too often overconfident or dismissive of contrary opinions, and we rationalize past failures instead of learning from them.  Author Julia Galef sees the solution to this problem in what she calls the ”Scout Mindset.” Fundamentally, it is the motivation “to see things as they are, not as you wish they were,” which leads to better judgment and decision-making. After years of research, she concluded that understanding how to act rationally doesn’t mean that you will actually do so.

Being able to rattle off a list of biases and fallacies doesn’t help you unless you’re willing to acknowledge those biases and fallacies in your own thinking. The biggest lesson I learned is something that’s since been corroborated by researchers…our judgment isn’t limited by knowledge nearly as much as it’s limited by attitude.

Self-delusion commonly hijacks self-awareness when you attempt to explain to yourself and others past decisions that led to bad outcomes or failures. The best antidote to self-deception is cultivating a mindset of self-discovery and self-awareness. In its most basic form, it is paying attention, observing, and noticing how you think, act, and behave.

Growth Mindset

A growth mindset, as researcher Carol Dweck discovered,

“…is based on the belief that your basic qualities are things you can cultivate through your efforts.”

In her research, she found that a growth mindset creates a powerful passion for learning, as self-improvement is within your control, given the right effort and strategies.

People with a growth mindset demonstrate grit. They stretch themselves, take chances, and stay engaged despite setbacks. A growth mindset allows people to thrive during challenging times and, critically, reveals a motivation to learn. As organizational psychologist Adam Grant found in his research, there is a self-reinforcing passion for learning, and he noted that “the highest form of self-confidence is believing in your ability to learn.”

Dweck’s growth mindset framework is an important tool for raising awareness of how mindsets impact behaviors, and perhaps the most important is discovering the willingness to engage in highly self-directed learning.

Organizations can (and should) create conditions conducive to learning through formalized training and education, aligned incentives, and a culture that encourages personal growth, but change is ultimately up to individuals. Healthy individual mindsets coupled with cultures that cultivate and nurture growth mindsets create professional communities of lifetime learners focused on continuous improvement.

“Think-like” Mindset

As organizations build data-driven cultures, it’s helpful to draw attention to core principles of critical thinking from other people and professions.  For example, scientists in all fields use the scientific method to experiment and learn. Its disciplined processes allow them to test theories about how complex systems work, be it how drugs react in the human body, or breakthroughs in materials that enable reusable rockets for space travel.

In his book, Critical Thinking, Jonathan Haber talks about the importance of science serving as a model for systematic reasoning.

“With all its successes, science is often held up as a model for systematic reasoning. Yet if you look at science not as a unique activity engaged in only by special people, but rather as a cultural approach designed to slightly diminish the confirmation biases that tend to make all people (including scientists) believe untrue things, you can begin to see the huge payoffs that come from small improvements in how we think.”

Haber, Jonathan. Critical Thinking. The MIT Press, 2020.

This “think-like” mentality is the gateway to changing mindsets as it raises awareness and encourages growth but doesn’t threaten someone’s current identity. It’s different than searching for best practices, which involves finding situations as close to your own and copying those practices. Instead, the “think-like” mindset encourages searching for principles wherever they can be found in different professions, industries, cultures, or the natural world.

Think-like means trying to understand how others go about problem-solving, and adopting any of their principles and practices that could be readily adapted to your situation.  We are firmly in the analytics age, and the “think-like” requirement now is to think more like a data scientist and be more analytical in your approach to problem-solving. Using this effectively doesn’t require you to actually be a data scientist any more than understanding the scientific method requires you to be a nuclear physicist.

Mental Models Mindset

The simplest definition of mental models is that they describe the way the world works. They influence how we think, understand, and form beliefs. Let’s look at a few examples of mental models commonly employed when using advanced analytics, how they reveal flaws in our thinking, and how they can be used as corrective measures.

First principles thinking:  First principles reasoning helps clarify complicated problems by separating the underlying ideas or facts from any assumptions based on them. In other words, it’s a way to expose assumptions underlying your thinking and challenge what you think you know about a problem. This process requires you to keep digging, sweeping away unproven assumptions until you arrive at the facts.

One method, called the “five whys,” requires challenging each outcome with the simple question “why?” This technique, first formally used by Toyota as part of their Lean manufacturing process, is now a standard method for getting to cause and effect relationships.

Leading vs. lagging indicators: One way to think about leading indicator metrics is they measure the activities that lead to results. Amazon, a leader in using analytics to drive decisions, refers to them as “controllable input metrics.” By identifying, defining, measuring, and monitoring leading indicators, you can anticipate problems and intervene before it’s too late to fix them. You rely less on postmortem processes like the “five whys” and more on real-time monitoring, intervening, and implementing course corrections.

This method is an excellent way to operationalize a mental model. Rather than periodically sitting down and challenging assumptions underlying past decisions (e.g., postmortem), you set up metrics that challenge assumptions continuously. In other words, the metrics you set up constantly answer the “what” questions (and perhaps the “why” questions) in near real-time. And to the extent they don’t, modify what you are measuring or how you measure it.

For all you data scientists still reading, it should sound familiar, as it’s much like monitoring a model you’ve built!

Probabilistic thinking: Probabilistic thinking is the process by which you determine the likelihood of any specific outcome happening in the future. We engage in this thinking whenever we check the weather to see if it will rain or speculate about the next Super Bowl winner.

But we are not particularly good at understanding probabilities in our personal or professional lives. We tend to use imprecise language to describe the likelihood of an outcome and are overly optimistic about our future predictions. Being right and making correct predictions is important, but knowing why you were right is essential. Adopting practices like Bayesian updating can help maintain your outside view by constantly adding new information to your existing data to get closer to the ground truth.

Mindsets offer a systematic way to ensure your thinking processes are more disciplined and consistent, enabling quality decisions across your organization. Mindsets help get you out of autopilot mode, to stop and think. You can start by learning and applying the initial foundational and transformational mindsets discussed here by embedding them in your decision processes.

Ultimately, realizing the data-driven culture depends on changing individuals’ daily behaviors. Adopting mindsets that encourage taking the outside view will help to systemize your thinking processes and establish consistent behaviors that translate into powerful daily habits.