In today’s digital world, one can sometimes be overwhelmed by something called Big Data. “What is Big Data?” you may be asking. The quick answer to this question that I found on Wikipedia says that “Big Data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.” So, how do we deal with Big Data? And how does it relate to art?
One of the ways that we can deal with big data is through something that Franco Morretti, founder of the Stanford Literary Lab, calls distant reading. Distant reading is a practice where scholars can understand a corpora not by studying particular texts, but by aggregating and analyzing massive amounts of data at once. We need distant reading because it can uncover the true scope and nature of a corpora that the more traditional close reading cannot. For example, this video does a pretty good job of explaining distant reading and how it can be used by humanities scholars, like historians:
As an Art Historian, the concept of Big Data and distant reading doesn’t seem all that comprehensible to me because I tend to think in visual terms, BUT lucky for me Big Data is usually represented through visual forms. This makes Big Data more legible and also aides in facilitating distant reading. This happens through a technique called information visualization or “infovis” for short. Infovis utilizes computer graphics and interaction to assist humans in solving problems and to visually represent data sets. Information visualization of big data can be used to support arguments about the past in ways that traditional humanities scholarship cannot.
In Art History, I use visual arguments all the time, however I am not used to the kind of scientific-esque visualizations of data (i.e. graphs, word clouds, etc) that are used to represent big data. I learned all about infovis the other week in DIGH5000, and of course, I got interested in the possibilities of these representations as a visual form. As Eric Rodenbeck noted, “information visualization is becoming more than a set of tools, technologies and techniques for large data sets. It is emerging as a medium in its own right, with a wide range of expressive potential.” What is interesting here is the use of the words “medium” and “expressive”–words that I usually associate with artistic practice.
So can Big Data and distant reading be turned into art through infovis?
I would say yes. Art, as I understand it, makes visible the invisible, and this is the same principle behind information visualization: it makes visible the trends and themes of Big Data. Scholar, Tom Corby would also agree with this. In his article, “Landscapes of Feeling, Arenas of Action: Information Visualization as Art Practice,” he discusses how certain artists are using information visualization to make art, and he also looks at how these data visualizations are used to inform various governmental policy in areas, and how they can, in turn, impinge on our social lives. For example, Abigail Reynolds’ Mount Fear East London (2003) visualizes police data collected over a one-year period in east London that was run through a 3D modeling program and then mapped onto specific geographical locations pertaining to the event. From this model, Reynolds developed a sculpture that dramatizes the data by transforming it into a mountains, with peaks and valleys signifying high and low levels of violent incident.
This sculpture could be used as an analytical tool to determine where more police resources or social programs are needed in London. In this way, the sculpture could inform governmental policy. It exposes the hidden social behaviours of society in subtle and critical ways. Furthermore, by occupying a physical space in a gallery, the sculpture enables the audience to reconnect to the situated acts they measure in a way that is not purely aesthetic, but in a way that conveys its meaning through interpretation. This experiential approach also seems similar to what Tim Hitchcock suggests in his article, “Big Data for Dead People.” He suggests that in some situations we cannot just read data, but that we must also think about including a sensory or experiential reading of our object of study if possible. For example, including a haptic experience or aural history to enhance the understanding that distant reading provides about a corpora. Each of these different kinds readings–distant, haptic or aural–change how we experience and relate to the past and to the data it has given us.
This sculpture also poses some issues concerning surveillance and the dangers of stereotyping a neighborhood. Big data needs to be interpreted, but it can only present what is being analyzed so we don’t get a complete picture–and this is something that Brian Croxall gets at as well. I think we need to come to conclusions based on the statistical or textual data, but we also need to have a touch of skepticism about how the visualization reflects the data. This is where I think distant and close reading come together in data analysis. In this way, when we create data visualizations we open a conversation about how big data can reflect our past and our world back to us.