Extra Credit: Secrecy, Intelligence, and Big Data

As Dr. Hugh Gusterson began his presentation on secrecy and intelligence, I must admit I found myself wondering how it pertained to big data, or if I was even in the right room. However, once he began to delve further into the discussion of the material, everything started to click, and I found myself making connections between what he was talking about and what we’ve spent so much time mulling over these past few months. Ultimately, among the many themes in Dr. Gusterson’s talk, I found his concepts of systematic divide and secrecy overlap to be the most salient.

The first theme I took away from Dr. Gusterson’s lecture was the systematic divide created by tactical uses of secrecy and classification. According to Dr. Gusterson, secrecy protocols at Livermore Laboratories created solidarity, hierarchies, and what he referred to as a “caste system” among those whom did or did not have clearance to intelligence. This can be approached through a big data paradigm, as the idea of access is what it really boils down to–the dichotomy between those with access to the resources, technology, data, and information, and those without it. This concept is echoed by Viktor Mayer-Schonberger, who writes, “The primary substance of big data is the information itself. So it make sense to look first at the data holders. They may not have done the original collection, but they control access to information and use it themselves, or license it to others who extract its value.” Essentially, the power resides with whomever controls the data, as well as anyone willing to pay the right price for a piece of the proverbial pie; everyone else is pretty much excluded. Whereas those with “clearance”–or authorized access–to data, are elite, those without the means of obtaining it or controlling it are merely the bottom of the totem pole with a decided disadvantage in terms of mobility.

The second theme I took away from the lecture is what I could best coin as “secrecy overlap”, or how the world of secrecy and classification coexists and overlaps with what Dr. Gusterson refers to as the “open world.” This is perhaps best likened to the overlap between the data people are content in making public, and the data they’d rather keep private. Oftentimes, there is a lot of intersection between what we make public and why we make it public, and what we keep private and why we keep it private. The driving force behind this intersection, is the value of the respective information attached to it. In the words of J.J., “Big data is valuable precisely because it can combine data in new ways that were not known at the time of collection.” Through a big data lens, it could be said that the data we actively share resides in the “open world”, and the data we keep to ourselves–or at least try to–is classified. It was extremely eye-opening to hear Dr. Gusterson, commenting on the dynamics of over-classification, say that the U.S. spent 15 billion dollars on classifying information–or 201 dollars per every 1 dollar on declassification–in 2014 alone. If the government is willing to exhaust so much in funds to keep particular information in their hands, I wonder to what lengths large companies are willing to go in order to get their hands on our data.

Dr. Gusterson’s talk had a surprising breadth in terms of big data applications and implications. His conceptual connections of systematic divide and secrecy overlap are microcosms for the contemporary big data climate. Components like secrecy, clearance, classification and the ethics of such discourse, speak to broader structures of power. Ultimately, just as those to whom clearance is granted hold the key to important information, parties who regulate the data hold the key to an invaluable wealth of knowledge and power. This, among other reasons, is what makes Dr. Gusterson’s insight so relevant to big data and how it will continue to shape our socioeconomic landscape moving forward.