You know what is sexy? Presentations where the data and algorithms presented by researchers come with a healthy does of real life context. [Also, other researchers who read applied statistics textbooks in coffee shops early in the morning. I have been doing this a lot recently and just made friends with someone who was reading a different book by the same statistician I was reading.]
I constantly complain that we lose a lot of information when we work with big data analytics. Part of it is that many researchers are encouraged to work with data from their desks in offices tucked away inside of universities or office buildings in major cities, far away from the ecosystems they are trying to describe through numbers and algorithms.
Nate Silver spends a lot of time talking about the weakness of prediction models in his book The Signal and the Noise: Why so many predictions fail — but some don’t. He points out that economists have trouble identifying relevant variables to make predictions. This is fair… economies are constantly changing in structure and dynamic. It would be really hard to collect appropriate data on the formal economy as it shifts, and even harder to keep track of informal economic activity in a way that would lend itself well to predicting output for the future.
I’ve found the only way that I truly understand the pulse of an economic ecosystem is by living and breathing the structure and community of it. After all, economies depend on communities and trust for transactions to take place at all. But this is for another post.
But I did find someone trying to add context to big data!
I watched this talk by Anna Rosling Rönnland from TEDxStockholm yesterday, and while the introduction is a little confusing, the center of the talk is important. The best way to watch this talk, in my opinion, is to consider the implications of using photographs to describe the spread of the distribution.
In non-jargon speak, this means, consider how your perspective on wealth disparity changes when you see how people in the richest 25% versus the middle versus the lowest 25% brush their teeth. This hits home a lot harder than quoting per capita numbers at someone would, because it also takes into account differences in pricing/living costs within the country. We can see where wages fall short and what that means in the day to day life of workers around the world. We gain perspective on data. And that’s sexy.