This was a talk that changed my life as a young adult. Misha Glenny is a journalist know for his work on criminal networks and organizations. I saw this talk and was hooked, shifting my research focus from immigration to black markets and organized crime. I purchased his book McMafia at TEDGlobal and devoured it.
I became fascinated with the different ways people organize and problem solve in the market, when they are either blocked from entering the traditional workforce, cannot build a business within the legal structure, or need to participate in black markets, rather than formal markets, for other reasons.
I gave a talk at the University session of TEDGlobal 2014 in Rio and Misha was in the audience. When I saw him later at the conference and he told me I had done a good job, it was like having a personal hero pat me on the back. (I gave a similar talk later that year at TEDxMunich)
I am constantly inspired by Misha’s research methods and writing style, and was so grateful to find his work at that point in my life. It helped me build my structure for study in University and beyond. That’s a powerful talk!
It’s a little bit like early product testing… for information based products.
As I read for edits, I look for each building block of a talk to see what kinds of questions and interpretations might come from our audience. I read for clarity and consistency with the research presented by the author and their research community. I read to make sure that audience can follow the definitions and other “tools” offered by the speaker.
I also consider a few other questions:
Is the data strong enough to make this argument?
How were the questions posed in the research process?
Did the author translate the context of the study clearly into their talk?
Once we finish with these edits, we know that we’ve at least “stress tested” the text before the speaker presents their work. And, usually, they feel better after we’re done and know we’ve taken this precaution.
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.
In The Signal and the Noise: Why so many predictions fail — but some don’t, Nate Silver states that “one sign you have made a good forecast is that you are equally at peace with however things turn out — not all of which is in your immediate control.” 
I think this concept applies to far more than just predicting weather forecasts, stocks, or how well particular baseball players will perform over the course of their careers. This applies to decisions that we make and how well we do our work.
I know that I’ve done a good job with my research, or really, my work in general, when I am comfortable presenting it and leaving it there to speak for itself after I present the work. When I have truly done my best, I am comfortable walking away from the work. It can exist independently, without me.
The ideal for any organizer is that your program will continue running without you, even if you quietly disappeared. The ideal for any researcher is that the work has merit and value, even when you are not there to carefully re-explain it.
So that is what I strive for. When I complete work, am I at peace with it? Does it have the legs it needs to stand on its own. Am I able to grant the work its independence?