Like moths to a light | Business Uncertainty series
False construct #1: We gain certainty from measuring what we see
Many of us will have heard of the parable of the man looking for his lost keys at night under a street lamp. He’s there on his knees searching away when a policeman walks up and asks if he’s sure he lost them there. And the man says no, he lost them in the nearby park. The policeman asks the man why on earth he’s searching for his keys here, and the man replies “because this is where the light is”. [2]
This parable is often called “the streetlight effect” to illustrate the point of observational bias.[3]
The challenge is that to feel more certain we often look where it’s easiest to look, not where we need to. We often look in the wrong places, and we measure the wrong things. And we often measure the symptoms, not the underlying causes.
As business leaders, how much energy is given to getting easy data where the light is brightest (e.g. sales pipeline measures, net promoter scores and social media polls) versus where the truth might actually lie? So often when I work with clients, business leaders tell me what customers want or what the market need is, referencing this constructed data. It’s useful but it does not give insight, and it certainly gives no certainty from which to make decisions off.
In design thinking we go to the park. We go out and connect with the world - real life, real time, with real people. We observe. We listen. We let life play out and we gain deep insight from that. It helps us connect with where the problems and the opportunities really are, at the source.
Certainty comes from this connection.
Don’t be a moth.
Given we respond to what we measure, how much organisational energy in your business is going to where the light is?
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[1] 2016 Study of 45 study participants as reported in Nature Communications. https://www.nature.com/articles/ncomms10996
[2] As an interesting aside, the US Indologist Wendy Doniger uses this parable in her book The Hindus: An Alternative History to illustrate that the “available light” on Hinduism tends to illuminate the perspectives of dominant groups. And the perspectives of marginalised groups, of women, of lower castes are relegated to the shadows. A parable in itself around how history, market systems and social class structures concentrate in the light of dominant forces.
[3] If you’re in the game of using big data, it’s worthwhile spending more time understanding observational bias and managing the very real systems-wide risks of false-positive inferences.