Data and the certainty deception

The problem with data is that it often gets interpreted as infallible, providing a false sense of comfort about our decision making. It’s why so many companies have made increasing investments in data analytics in the past decade.

But data is not insight—at least, not by itself. And there are three often unacknowledged thought traps that happen in the interpretation process:

The best data has to be interpreted by people, and people are fallible, prone to all sorts of biases and logical fallacies.

Most data is meant to be read as a snapshot in time, and the world moves quickly enough that it should be seen as having an expiration date.

The world is too complex for any small number of data points to tell us all we need to know.

For these reasons, data can be both empowering and paralyzing, depending on the organizational culture that surrounds its interpretation. (Not for nothing, I wrote a lot about this in my book, talking about my own experiences with fast-moving analytics teams.)

Don’t let the need for certainty keep you from examining the interpretation process itself. Recursive self-reflection should be a part of the culture of every analytics team.

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