Digital Divide Research as a Practice of Big Data

Big Data seems to be the new buzzword of the moment and the solution to all of society’s problems. Often we hear people coming up with studies involving a great amount of data aggregated from Twitter, Facebook and so on. I truly believe these studies are good; they take snapshots of scenes, let us know of interesting moments in a specific time and give us an overall idea of the problem.

boyd and Crawford (2012) define big data as “a cultural, technological, and scholarly phenomenon that rests on the interplay of: (1) Technology: maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets. (2) Analysis: drawing on large data sets to identify patterns in order to make economic, social, technical, and legal claims. (3) Mythology: the widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy.” (p. 663)

Big Data is usually thought as big numbers, the big N approached quantitatively. These numbers are generated based on people’s produced data; people that are online and constantly talking, sharing, posting, tweeting and “liking” things. But what about the people that are not doing that frequently, or even, not doing these activities at all? If we take Big Data and extend it to the ones experiencing digital inequalities, we would be imposing a colonial practice in which the voice of those constantly online will be obscuring the voice of those who are not. These voices are often clashing in different of contexts since they are rooted in social tensions and differences of power.

So, how can Big Data tell us the story of the people that are on the “wrong” side of the digital divide?

Mary L. Gray (2011) makes the case that Critical Ethnography is a practice of Big Data. She invites us to think of Big Data not solely as numbers and quantitative approaches, but also as a practice that is able to balance the value of ethnographic significance and statistical significance. Big Data is usually deeply concerned in mashing as much number as possible to be able to have some sort of reliability and statistics strength. The more you can get, the more reliable the information is.

Qualitative work is often seen as being too specific and doesn’t tell us anything, but Gray argues the opposite, qualitative approaches tell us something different, they give us a different perspective of the story. Ethnographic significance should be integrated as a complement in collaboration with statistical significance, so we are able to get something transformatively different.

I agree with Gray; at an earlier post here on the Social Informatics Blog (Digital Divide Research: one myth, problem and challenge) I make the case that the Digital Divide Research should move on from the statistical charts, census and Big Data, and go in the field to tell us about the context of those who are not on the internet, or not as often due to digital inequalities.

Big Data was the reason why I ended up going to the slum of Gurigica in Vitoria, Brazil. According to the census, the locals have a very low access to the LAN Houses and Telecentros that are inside the community. But if it wasn’t for my ethnographic research, I would have never known that this was happening due to the activities of the drug cartel that didn’t allow them to circulate freely on the streets. Therefore, Critical Ethnography is a powerful tool to approach the issues of the Digital Divide and contextualize the notions that Big Data gives us.

References (I highly recommend Gray’s video):

danah boyd, & Crawford, K. (2012). CRITICAL QUESTIONS FOR BIG DATA.Information, Communication & Society15(5), 662-679.

Gray, M. L. (2011). Anthropology as BIG DATA: Making the case for ethnography as a critical dimension in media and technology studies. http://research.microsoft.com/apps/video/default.aspx?id=155639

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About davidnemer

Assistant Professor in the School of Information Science at University of Kentucky.

Posted on March 6, 2013, in Big Data, Critical perspectives, Digital Inclusion and tagged , , , . Bookmark the permalink. 2 Comments.

  1. Great points and totally agree with the article. Funny thing is that I am a quantitative researcher for the most part. Over time I slowly discovered that by including open-ended questions in my surveys will provide me with insights that numbers never could. They don’t only explain why there are outliars in a dataset but they also explain why trends are the way they are.

    In respect with digital divide research, I think the issue is the attitude. People look at a problem (by looking at the numbers) and say let’s just throw lots of money at it. It is well-founded that money matters when people are talking about access, speed etc. But, there are other things to consider and culture and local politics play a huge role. When I was growing up, computers and the internet was truly a social suicide. You want to be cool then DON’T use them. It is just for people wanting to see the stock market. Now, I was a geek, but I can’t help but wonder if there were people that stayed clear of technology for a couple of years because they didn’t want to be treated as social outcasts.

    Btw, it is funny that we put so much weight on economic factors for the digital divide when prominent theories such as the diffusion of innovations identify human capital as a huge factor for adoption of new technologies.

  2. Good stuff David… I’m wondering whether in fact Big Data could have had the insight you had re: the drug dealers and the DD… It would be interesting to test that out… (The CIA CTO whose talk is currently going viral suggests that they probably could figure it out if they wanted, if not now then soon. http://www.huffingtonpost.com/2013/03/20/cia-gus-hunt-big-data_n_2917842.html

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