Information Overload

Exploring the impacts of Big Data at the UA College of Science Spring Lecture Series

Vincent del Casino Jr. is UA vice president for academic initiatives and student success as well as professor of geography and development. At 7 p.m. Monday, Feb. 26, del Casino will be presenting a talk entitled "There's No Such Thing As Big Data" as the final lecture in the UA College fo Science lecture series at Centennial Hall. Del Casino recently discussed his work on the televised edition of Zona Politics with Jim Nintzel. This Q&A is an edited and condensed transcript.

Your talk is focuses on the idea that "There's No Such Thing as Big Data." Big data is the idea that you gather all this information about hundreds of thousands or millions of people and are able to sort through it and figure out patterns and trends and whatnot through that. So when you say there's no such thing as big data, what do you mean?

Well, I'm intentionally being a bit argumentative with the idea, in part to say that what we've done socially is take this thing called Big Data and made it a thing—which is why I use the singular, because data is actually plural. Big data has become this big thing and we're not looking at it for all its component parts and all its complexity sometimes. What are the various pieces that are informing who we are and what we think about? What's the plus side of having all this information about people and what you can do with it? It's a really interesting set of questions because there are lots of possibilities for doing really deep, interesting sorts of analytics when you have these large data sites and you can go in and really ask some real provocative questions. A colleague of mine who's in computer science does work on millions of articles a year in science and medicine and has actually been able to look at all these studies in a way that humans can't. Humans cannot simply read a million articles a year. It's just not going to happen. But computers can start to do that sort of work so being able to get into these data and start to extract meaning and ideas and connections and so forth is really interesting.

Is there a danger to it?

Well, there's a challenge to it, a couple of challenges. The first of which is: It becomes kind of a black box and we don't really know what's going on in it, so what is happening and what's going on within those big data analytics is a question. The second thing is, we start in some ways to forget that there's this robust engine of data analytics working behind the scenes and we just start interacting with it and it becomes kind of our natural every day and we don't always stop to question, "How did that come to be part of who I am?" "How did my data go out into the world and then come back to me in a different way?" And there are some really interesting privacy and ethical concerns that we have to think about.

The government is collecting a lot of information about us. The private sector is is probably collecting far more information about us. What are the privacy dangers?

How many people pick up a device every day, get an application and then rifle through the privacy notification and just hit OK? We're voluntarily giving over our information all the time and they're collecting this up and they're making composites of who we are out of these experiences and then feeding it back in a benign way. That means Amazon gives me book options or Facebook throws some ads. But we saw what happened during the elections of the ways in which those algorithms start to generate news stories that I'm supposed to digest versus other people. There's so much going in and out now that it's very hard for an individual to manage or even understand where their data is going and what's happening to it.

Your work is developing strategies for student success at the university. How does gathering this data about students help you with that?

In the old days, we used some pretty crude measures to determine whether or not a student might have success at university. We might look at SAT scores, GPA, some things like that. And then we'd start to maybe try to find programming for them. Well, you can go to much larger scales in terms of the nuances that you might be able to engage. In fact, one of the things we've done at the University of Arizona is use predictive analytics to start to look at what are the other sorts of component that might put students at risk for dropping out of school or something like that and we've we found within the first four weeks of school, we can start to see behaviors within the data that suggests that a student might be a greater risk for leaving

And then you could intervene.

Yeah, you can intervene. The big question is, again, what are those data? And to what extent is the University responsible or informing people of how we're using those data and what what data are being collected.

You've told me the UA has been collecting data of whether they go to the rec center, for example.

We have. Students use our Cat Card and they swipe their data and we know where students go and we've actually been able to show that students who go to the recreation center more than once a week actually have a much greater likelihood of staying at the university. So it's a really nice finding but it's also an interesting question of to what extent we have to start to have conversations with our students about what's going on in their lives. I think what's coming next is: How much are we going to be able to demand of governments or private corporations or our public education systems? What do you have on me and can I see it all?

How do you see higher ed changing because of this data collection and other trends? You've said we're moving past multidisciplinary to possibly post-disciplinary studies. What do you mean by that?

There's a rapid change of the economy and society and politics and everyday life. So how are we preparing and training students for the future? We've been organized, in higher education for the last 150 years or so, around disciplines. I'm a geographer, that person is an anthropologist. People talk about interdisciplinary ideas. We'll bring these people together, but the data actually don't care about whether I'm a geographer. They're beyond what I think the discipline is. That's where I start to think about post-disciplinary. Should we start to train people and particularly undergraduates not around disciplines but around questions about big ideas, about grand challenges? And do we have to adapt our curriculum and our thinking to those kind of questions?

There are many people who can't afford smartphones and computers. That's the so-called digital divide. How does that affect this collection of big data?

It's a really interesting question because it's becoming more and more difficult to look at yourself and think about how you're going to advance yourself or your family without engaging these sorts of things. But the divide is real, and you can see it right here in Southern Arizona. We don't have to go further than probably 20 miles outside the city of Tucson to find those disconnects from these sorts of things, and the questions that are going to arise are not just about my intersection with data but my ability to understand the data. And secondarily, to access the economy of the future—"What is my ability to get that job?"—when we're automating so many different things. I don't think we're going to end up with rapid unemployment. Some people will fear there won't be any jobs left. The robots will take them over, will automate everything. I don't actually buy into that. I think the economy is going to be very different, though, for what humans do and who's going to be prepared for that. Is it going increase inequity over time? We're got to figure out how to level that playing field now.

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