We think we know more than we actually do. In fact, we are wired that way. This illusion helps us to get along in the world. But it also gets us into trouble sometimes. Like when we are planning what our organization should do next.
Overtime, we have relied less on our own abilities to build houses, cure diseases, or fix toilets and more on others’ knowledge in these areas. We each specialize, gaining more in-depth knowledge in one area than any generalist could. Then we trade our knowledge for that of others. Seems like a good idea, but there are downsides.
We so effectively collaborate that, as Sloman and Fernbach argue in The Knowledge Illusion: Why We Never Think Alone, the lines between our understanding and that of others blurs together. We perceive the others’ knowledge as our own, even when that “knowledge” is actually baseless opinion. “This is how a community of knowledge can become dangerous,” according to Sloman and Fernbach.
Every organization has its orthodoxies, but not all of them are true. How can we distinguish the truth from our own and others' deeply-held but false beliefs?
The answer is: data. In other words, we can use the scientific method and put our assumptions to the test. If we cannot find evidence (aka data) that sufficiently refutes our assumptions, we can feel encouraged that we MIGHT be right, as long as new data doesn’t come along and undermine our beliefs.
Progress in organizations — and in all of human history — starts with the concession that we might be wrong. As Yuval Noah Harari suggests in Sapiens: A Brief History of Humankind, the scientific revolution was the point in history when “humankind admits its ignorance and (as a result) begins to acquire unprecedented power.”
On a more modest scale, we can start asking questions like: what would we expect to see in the short and long run if our programs work how we expect them to? And then we can look for data that either supports or refutes our expectations. And if we bristle at spending our time with data when so much else needs to be done, we can make data more digestible by visualizing it. (See Data Tip #1 for more on the power of data visualization.)
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.