Predictive Marketing Blog

Are We Too Dumb to Use Data?

Are We Too Dumb to Use Data?

I have to be honest. After the lewd conversation between Donald Trump and Billy Bush surfaced recently, I wondered if it was the final nail in the coffin for the Trump campaign. Yet, as FiveThirtyEight details, GOP leaders are unlikely to pull the plug on the party’s candidate. Not entirely, anyway.

At the same time, our culture seems fractured on other important issues like climate change, genetically modified organisms (GMOs), and flossing our teeth – even though we have scientific data to support certain positions.

This got me thinking: we have all of this data available on which to base our decisions, but are we too “dumb,” aka biased, to use it?

In other words, does data even matter, if we have already made up our minds?

The Facts about Near-term Data

According to The Software Alliance, 90 percent of the world’s data was created in two years’ time. This is why we see lots of startups focused on data – from storage to analysis to predictive – and lots of organizations that want to learn how to leverage it.

Why do they want to leverage it? Because it tells them more about their existing customers, it allows them to find new customers, and it empowers them to sell more to both of these audiences.

But when it comes down to it, the data is only as valuable as our beliefs allow it to be. We see evidence of this in what psychology deems confirmation bias – where decision makers seek out information that confirms their beliefs. We aren’t looking for anything new, just a nod in the general direction of what we think is correct.

This can be a dangerous posture, though, when it comes to using data in the digital era. If marketers are only able to use information that aligns with pre-existing assertions, then they may be unable to move away from the familiar in order to increase their reach to new and sometimes equally influential audiences.

This is an important distinction, as marketers start to run tests using all kinds of data, we now have to be careful to question if we’re running the wrong kind of tests or creating algorithms according to our biases. We can see the fallout of this if we look to the somewhat recent revelations of political bias at Facebook, where workers routinely suppressed conservative news.

By influencing our testing, we influence our data and, therefore, our business decisions. If we are going to recognize the full potential of all of this incredible data, then we have to start thinking about information as a way to grow the business – even if it goes against our own assumptions.