It’s amazing to see the use of technology to track, monitor, and collect information and data from things that we do, like sports, to help improve and enhance what we do. I think it’s called life-hacking and if you’re thinking of resolutions for the New Year, there’s a whole website about doing just that. Yet as we get more and more comfortable tracking and hacking ourselves (FitBit anyone?), I can’t help but think – are we getting too reliant on the data?
I’ve written a couple posts about how data has been used to better predict and inform baseball and basketball. Yet a prime example of the over-reliance on data, and not balancing it with common sense, comes from the sports world. In this article, it shows that despite all the hype around big data for the 2014 World Cup, Nate Silver, a popular data geek who made predictions during the 2012 US presidential election, still got it all wrong. Some said that the competition was no place for big data, which can’t understand the intrinsic issues and subtleties that real soccer fans see. Others claimed that Silver ignored some basic data issues.
Data and information that we collect whether through technology or by ourselves inherently has biases, like how someone setup these exit “sortie” signs for a reason. We build the technology and develop algorithms that are supposedly objective yet in developing them we make inherent compromises and assumptions. The same goes with collecting and compiling data ourselves – from monitoring our diet, building a contact/email list, or just keeping track of our to-do lists and calendar – we are biased to certain things (ex. what we think is more important, what we can remember, etc.) when collecting this information (i.e. Excel sheets anyone?). Also are we managing our information consistently enough so that it can reveal some truth that can help our decision-making?
I work for an organization that prides itself on its “information management” and spend a lot of time with internal and external clients to not only improve this management (ex. simplify, organize and clean the data), but also to understand how it can be used strategically to communicate their work and key messages (ex. like making a good infographic). Within the international development community, OCHA is light-years ahead of the game when it comes to this. They’ve also evolved and branched out to apply information in a useful way for the humanitarian community like the recently launched INFORM initiative to improve risk analysis and the Humanitarian ID project to make contact management simpler and better for an emergency or crisis. Perhaps following OCHA’s lead, there are plenty of UN organizations starting to visualize this information and realize that data is more than just 1’s and 0’s or that it’s only for “geeks”, but can be used in different ways to communicate and provide “evidence” to improve programming and decision-making. The success of innovative ideas like these will depend on how accessible both the data and tools are to the people who will use them. It can also be summed up by these two quotes from the Nate Silver article:
Predictions are no better than the quality of data and model that you employ.
Big data and predictive techniques are supposed to inform smart decision making, not automate it.
On the opposite end of the spectrum of the Silver article are these little visual vignettes by the New York Times of what went right for the dutch, and so wrong for Brazil during the world cup. They are both data-driven and informative.
Data simplicity might be the best way to help us understand and improve the way we do things.