When I was researching at Nokia in 2009, which at the time was the world’s largest cellphone company in emerging, I discovered something that I believed challenged their entire business model. After years of conducting ethnographic work in China from living with migrants to working as a street vendor and living in internet cafés, I saw lots of indicators that led me to conclude that low-income consumers were ready to pay for more expensive smartphones.
I concluded that Nokia needed to replace their current product development strategy from making expensive smartphones for elite users to affordable smartphones for low-income users. I reported my findings and recommendations to headquarters. But Nokia did not know what to do with my findings. They said my sample size of 100 was weak and small compared to their sample size of several million data points. In addition, they said that there weren’t any signs of my insights in their existing datasets.
In response, I told them that it made sense that they haven’t seen any of my data show up in their quantitative datasets because their notion of demand was a fixed quantitative model that didn’t map to how demand worked as a cultural model in China. What is measurable isn’t the same as what is valuable.
By now, we all know what happened to Nokia. Microsoft bought them in 2013 and it only has three percent of the global smartphone market. There are many reasons for Nokia’s downfall, but one of the biggest reasons that I witnessed in person was that the company over-relied on numbers. They put a higher value on quantitative data, they didn’t know how to handle data that wasn’t easily measurable, and that didn’t show up in existing reports. What could’ve been their competitive intelligence ended up being their eventual downfall.
Since my time at Nokia, I’ve been very perplexed by why organizations value quantitative more than qualitative data. With the rise of Big Data, I’ve seen this process intensify with organizations investing in more big data technology while decreasing budgets for human-centered research. I’m deeply concerned about the future of qualitative, ethnographic work in the Era of Big Data.
Il aurait été utile d’expliquer aux dirigeants de Nokia pourquoi Big Data nécessite d’avoir une vision d’ensemble (the Big Picture). De fait, c’est comme si en période de pandémie de grippe, vous décidiez de prendre un traitement pour la grippe simplement parce que vous avez tous les syndrômes de la grippe. Vous le faites sans aller voir un docteur et sans vérifier que le traitement fonctionne. Or, la loi des grands nombres ne dit pas que vous avez la grippe, elle dit que vous avez probablement la grippe. Mais il existe d’autres maladies avec les mêmes symptômes. Par exemple la Leptospirose (–) qui est mortelle dans 5% à 20% des cas.
Big Data un outil clé pour la Smart City 3.0