Decision-Making

Data doesn’t say anything. Indeed, data can’t say anything for itself about an issue any more than a saw can form furniture, or a sauce can simmer a stew.

Data is inert and inanimate. Data doesn’t know why it was created. Data doesn’t have a mind of its own, and, therefore, it can’t infer anything.

Data is a necessary ingredient in judgment. It’s people who select and interpret data. People can turn it into insight or torture it to bring their agenda to bear. Data is therefore only as useful as its quality and the skills of the people wielding it.

Far more than we admit, subjectivity and intuition play a significant role in deciding how we collect, choose, process, explain, interpret, and apply the data. As entrepreneur Margaret Heffernan warns in Willful Blindness: Why We Ignore the Obvious at Our Peril (2012,) “We mostly admit the information that makes us feel great about ourselves, while conveniently filtering whatever unsettles our fragile egos and most vital beliefs.”

In the hands of careless users, data can end up having the opposite effect its creators intended. All data is good or bad depending on how it’s employed in a compelling story and what end it’s serving—neither of which the data itself can control.

• Don’t let data drive your conclusions. Let data inform your conclusions.
• Don’t declare, “The data says,” (as in, “the stock market thinks.”) Data by itself cannot have a particular interpretation.
• When you find data that seems to support the case you wish to make, don’t swoop on it without caution and suspicion. Data can be very deceptive when used carelessly.
• Be familiar with the limitations of your data. Investigate if your data informs any other equally valid hypothesis that could propose an alternative conclusion.

Idea for Impact: Beware of the risk of invoking data in ways that end up undermining your message.