On Saturday, September 27th, I went with several friends to the Big Data exhibit and walking tour. I had never been to the Chicago Architectural Foundation, or seen the scale model of Chicago before. Being able to look at the city from a birds-eye view was an insightful look into how organized, massive, and sprawling the city of Chicago actually is.
After several minutes of observing the model, and trying to find the exact locations of Loyola’s buildings, it was time for the tour. We suited up with Ipads, touristy headphone attachments, and went on our way. Our guide, Lisa, a 60-something Loop resident, was clearly passionate about architecture. Data and technology? Not so much.
The tour was interesting, and but was more of a basic overview than an in-depth exploration of the way Chicago uses Big Data. For example, Lisa could tell us that there are 3,000 Divvy bikes and 300 stations in Chicago, and that more than 12.5 million miles have been ridden since Divvy came to the city, but that was where the knowledge stopped. When I asked what Divvy was doing with that information, she didn’t know. This was the theme of the tour. Lisa noted that traffic is calmer (think slower) due to increased bike lanes in the Loop and on Dearborn street. That makes sense.
However, when the question “Does the increase in bike traffic reduce car traffic enough to offset the longer traffic time?” Lisa had no answer.
The key takeaway from the tour and the exhibit, at least to me, is that Chicago and the various departments and companies within it are gathering massive amounts of data, but they don’t communicate with each other to be able to efficiently and effectively use it (this was the impression I received). Additionally, where does all this data go? Is it in some hard drive in a dark room in City Hall? Do you have to “know a guy who knows a guy?” Information on this scale, about the people in the city, should be available to those people, not only due to privacy concerns, but also because someone out there might have a better, cheaper, more innovative solution to any problem, or a creative idea to address a previously unforeseen issue.
The idea behind the exhibit, and the data behind the exhibit, were incredibly interesting and thought-provoking. The implementation, not so much. I will pay attention to the way Chicago uses Big Data, but not through a basic overview.