This week’s talk was presented by Aaron Hanlon, an English professor here at Colby. He spoke on the subject of revolutions involving the way we think about data: What it is, how we visualize it, and what it means for our understanding of data-driven fields of study. He started off by arguing that data is an inherently visual thing. From the first uses of the word data, it has constantly been the goal of the presenter to present data in an easy to digest form, even when data was used in a strictly scriptural context. Theologians argued that to convey the essential “truths” of scripture, one should use simple and precise words. When the concept of data in a scientific, observational capacity developed, people like Francis Bacon of the Royal Society revolutionized the term “data”. The age of enlightenment brought about a revolution in scientific thought, which diverged from esoteric, subjective narratives as seen through the eyes of a human observer, towards a data-driven, more objective style of scientific thought with developments such as Micrography which made science much more accessible to more and more people. This new focus on data and its use as visual evidence also revolutionized scientific accuracy by cutting out the margin for error caused by words, which can be influenced by an author’s opinion or writing style.
Another revolution then occurs as we approach the modern, more technological age in the form of what we call “Big Data”. Usually we think of big data as a unique revolution of the last few years with the advent of massive data collection schemes by corporations and governments, which harness user data to draw all sorts of conclusions. These can range from who to target with advertising on Facebook, to who to target with airstrikes from a drone in the Middle East, either of which can be determined by an individuals web browsing patterns. However, this recent conception of big data is only one version of the issue of big data. Since the advent of exponentially growing computing power in the 1990s, scientists have increasingly had to grapple with how to parse large amounts of computational data, and, with greater difficulty, effectively visualize it in an easily-understandable format.
Overall, I thought that Hanlon’s talk was interesting. I enjoyed his deadpan humor and sarcastic demeanor, and his conclusions were not at all pretentious or preachy. I’d say that his notion of data as visual evidence is indeed a revolutionary development, but it is also certainly an ongoing one, much like the other “revolutions” in this lecture series. As data continues to become increasingly cumbersome and difficult to represent, we will have to pursue subsequent developments in how to effectively parse and represent data that no human could hope to comprehend without visual representation. Hopefully, an increase in the general population’s understanding and awareness of the developments in big data will in turn lead to more concern over the ever growing abuses of big data by corporations and government using data to their advantage.