Ever since the Sloan Digital Sky Survey began amassing astronomical data in 2000 and gathered more data in its first weeks than had been collected in the history of astronomy, the term “big data” began its ascendancy. In many academic circles the possibilities big data offers seem brighter than the stars the Sloan telescope observes. If one were to tally how many times people reference “big data” in current higher-education discourse — in publications, at board meetings, by the coffee machine in the faculty lounge — that collection would itself constitute a data set of dizzying size.
Big data means just what it sounds like. In the sciences, more sensitive instruments — telescopes, particle accelerators — are able to gather unprecedented amounts of information. In the humanities, the digitization of texts, run largely through Google, allows scholars to potentially employ computational methods to help us better understand our cultural inheritance on a macro-scale.
When it comes to analyzing these huge data sets, however, many scholars are at a loss. Collaborative academic work seems like a logical step. Some academics want to go further and experiment with crowdsourcing methods of data mining — hence the rise of “citizen science,” where nonprofessional researchers help scholars gather and analyze data. Though we currently have too much measurement and too little theory, it is becoming more and more likely that big data is here to stay, provided our infatuation with information technology continues.
The corner of the University best equipped to incorporate big data into its curricula, thanks to its technological infrastructure and reigning scholarly interests, is the Engineering School. And with an article it published earlier this month, “Making the Leap from Data to Decisions,” the school took an aggressive stand on how thinkers should approach big data. The article’s place of publication — the Engineering School’s monthly online newsletter — was modest, but the piece’s thrust was bold. The article reported that the Engineering School hoped to take a leading role in harnessing large information sets to produce better decisions in fields including “marketing, manufacturing or medicine.”
As with any zeitgeist, the time to capitalize is short. The article quoted Engineering Prof. Barry Horowitz, chair of the department of systems and information engineering, saying his department had “a three- to five-year window of opportunity before other departments in the country recognize the complex relationships among human decision-making, analytical decision techniques and the large data sets that can be used to support those decisions.” In other words: the University has a chance to become a pioneer in using big data to better understand human behavior, but it must act swiftly.
Like the data its scholars seek to analyze, the Engineering School’s ambitions are big. The school deserves praise for its efforts to integrate big data into the undergraduate curriculum. But the Engineering School should seek opportunities to collaborate with other departments to explore big data. The department of systems and information engineering’s current campaign to use big data to improve human decision-making offers abundant opportunities for such collaboration. The Darden School is one possible partner. Corporate activity generates large amounts of data. Harnessing such data to improve organizational efficiency and make more responsible decisions would be a step forward for business education. And guidance from economists and cognitive scientists in the College could add depth and nuance to the Engineering School’s study of risk analysis and decision-making. Big data, after all, holds promise for more than just engineering disciplines — we hear it’s the next big thing.
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