Obsessed with data? It’s nothing without the human side


The drive to collect data has arguably never been stronger in the world of business, but one expert warns that founders are putting too much faith in numbers and ignoring the human qualities of their users.

Ethnographer Tricia Wong, who works with companies like Nokia to fill in the gaps left by big data sets, says too many businesses fall into the trap of trusting charts and figures without checking if the numbers align to reality.

In a TED Talk delivered last year, Wong outlines the key mistakes leadership teams make when using data, starting with her own experience working for Nokia.

Wong outlines how, despite collecting information on the ground in China and spending weeks speaking to mobile customers in different communities about their desire to buy smartphones, when she relayed her qualitative data sets to Nokia, there was a reluctance to believe the market was smartphone-ready.

The reliance on sets of big data on this topic meant the brand lagged in its strategy as the popularity of the iPhone started to accelerate across the region, Wong says.

It can be difficult for businesses to focus on small sets of opinions or first-hand accounts of users, but failure to do so can mean companies miss big trends.

“We have this thing called the quantification bias — the value of prioritising measurable things over immeasurable … The problem is that quantifying is addictive, and when we forget that and don’t have something to keep that in check, it’s very easy to dismiss information,” she says.

The best course of action for businesses is to focus on integrating qualitative and quantitative data, to make sure the data they are crunching is in line with people’s experiences of their products.

Wong suggests companies start collecting “thick data”, or stories from humans, to test against their other facts and figures.

“Thick data comes in the form of a small sample size but delivers incredible depth of meaning. Big data is able to offer insights at scale and leverage the best of machine intelligence. When you integrate the two, that’s where things get really fun,” she says. 

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