"More than machinery, we need humanity."
When discussing the digital, conversations can quickly shift towards talk of quantity. Just how many images are being uploaded every hour, how many meticulously monitored purchases are being made on a particular e-commerce platform every day, how many vehicles are being booked through a ride-sharing app at 3 p.m. on Tuesday afternoon, how many people are streaming how many shows/movies/albums at any given time? The specific answer to the “how much?” and “how many?” will obviously vary depending upon the rest of the question, yet if one wanted to give a general response across these questions it would likely be fair to answer with some version of “a heck of a lot.” Yet from this flows another, perhaps more complicated and significant question, namely: given the massive amount of information being generated by seemingly every online activity, where does all of that information actually go, and how is that information rendered usable and useful? To this the simple answer may be “big data,” but this in turn just serves to raise the question of what we mean by “big data.”
“Big data” denotes the point at which data begins to be talked about in terms of scale, not merely gigabytes but zettabytes. And, to be clear, a zettabyte represents a trillion gigabytes—and big data is dealing with zettabytes, plural. Beyond the sheer scale of the quantity in question, considering big data “as process and product” involves a consideration of “the seven Vs: volume” (the amount of data previously generated and newly generated), “variety” (the various sorts of data being generated), “velocity” (the highly accelerated rate at which data is being generated), “variability” (the range of types of information that make up big data), “visualization” (how this data can be visually represented to a user), “value” (how much all of that data is worth, especially once it can be processed in a useful way), and “veracity” (3) (the reliability, trustworthiness, and authenticity of the data being generated). In addition to these “seven Vs” there are also the “three Hs: high dimension, high complexity, and high uncertainty” (3). Granted, “many of these terms remain debatable” (3). Big data is both “process and product” (3), its applications vary from undergirding the sorts of real-time analysis that makes it possible to detect viral outbreaks as they are happening to the directions app that is able to suggest an alternative route before you hit traffic to the recommendation software (be it banal or nefarious) that forecast future behavior based on past actions.
To the extent that discussions around the digital generally focus on the end(s) results of big data, the means remain fairly occluded both from public view and from many of the discussants. And while big data has largely been accepted as an essential aspect of our digital lives by some, for many others it remains highly fraught.
As Natasha Lushetich notes, “in the arts and (digital) humanities…the use of big data remains a contentious issue not only because data architectures are increasingly determining classificatory systems in the educational, social, and medical realms, but because they reduce political and ethical questions to technical management” (4). And it is this contentiousness that is at the heart of Lushetich’s edited volume Big Data—A New Medium? (Routledge, 2021). Drawing together scholars from a variety of different disciplines ranging across “the arts and (digital) humanities,” this book moves beyond an analysis of what big data is to a complex considerations of what big data could be (and may be in the process of currently becoming). In engaging with the perils and potentialities of big data, the book (as its title suggests) wrestles with the question as to whether or not big data can be seen as constituting “a new medium.” Through engaging with big data as a medium, the contributors to the volume grapple not only with how big data “conjugates human existence” but also how it “(re)articulates time, space, the material and immaterial world, the knowable and the unknowable; how it navigates or alters, hierarchies of importance” and how it “enhances, obsolesces, retrieves and pushes to the limits of potentiality” (8). Across four sections, the contributors grapple with big data in terms of knowledge and time, use and extraction, cultural heritage and memory, as well as people.