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Most likely due to the fact that I study public health, the examples in the Mayan-Schonberger essays that resonated with me the most were those related to health issues — namely Google Flu Trends and the collection of vital sign data from patients. When bodily functions and disease are quantified in such a way, it is difficult to avoid criticism of these practices from the perspective of biopower. Population behavior is assessed and regulated.

When big data is used for biological purposes, the issue of time must be considered. Analyses such as Google Flu Trends and H1N1 occur more retrospectively — that is, during or after a pandemic — than prospectively. A person’s vital signs are taken once they have been admitted to the hospital, not preemptively.

The most prospective example was Target, who successfully identified a woman’s pregnancy and advertised relevant products to her. The capitalization upon this prediction likely affected the consumer decisions she made while pregnant (and possibly those that occurred after her child was born). There was a greater temporal distance — something I suspect will have much more significant implications for big data and its biological role in future years.

Rather than big data enabling the prediction of immediate solutions, at what point will it be able to make prospective decisions distant from an uncertain future in order to predict and regulate behavior years in advance? And how do our identities tie into these predictions (e.g.  a pregnant girl whose place of residence, income and other factors are likely discernible from data she unknowingly produces and can correlate with how she will raise a child)?

Finally, although biopower typically concerns state actions, Mayer-Schonberger’s examples demonstrate that a great deal of big data analysis is conducted by private corporations. Of course, companies such as Google are not completely separated by the state, but their activities appear to be more motivated by profit than the subjugation and regulation of populations. But as the possibilities (and limitations) of big data are beginning to illustrate, a synchrony between profit-making and regulation may be necessary in the age of big data.