It is easy to denounce data as a byproduct of capitalism; an analogy for materialism, classification, and competition. Contemporary data production is challenging to critique without citing modern society at large. Its roots, however, do not lie within the 21st century, as one might expect. From the beginnings on, humans have tried to find a deeper understanding and elevated consciousness of the world they inhabit. This essay attempts to uncover its roots, plot the medium’s boundaries in contemporary culture and reflect on the societal challenges it is to face in its future.
Unlike a computer, the human brain is particularly bad at processing large amounts of data at once. Worrisomely, as we enter the information age, data consumption has exploded, though it’s visualisation brings its very own societal challenges. Our relationship to data, and computers at large is complicated. Since the beginnings, the computer has been depicted as an analogy to the human mind. The notion that computer processes are an abstraction of human brain processes is widely spread throughout society. Computer scientists are unable to explain computational processes without referring to the brain as a comparison — yet the brain and computers share little in common. This faulty reference is at the core of our problematic data consumption.
But here is what we are not born with: information, data, rules, software, knowledge, lexicons, representations, algorithms, programs, models, memories, images, processors, subroutines, encoders, decoders, symbols, or buffers–design elements that allow digital computers to behave somewhat intelligently. Not only are we not born with such things, we also don’t develop them–ever.
— Robert Epstein, senior psychologist at the Institute for Behavioral Research and Technology in California
Research Scientist Andrew McAfee and Professor Erik Brynjolfsson of MIT claims that “More data cross the internet every second than were stored in the entire internet just 20 years ago.” In parallel to the increase of data production, we are increasingly becoming ill–equipped to understanding the world which we inhabit without tools to process it. Naturally, an accessible, expressive form has evolved to surpass the limitations of human perception. The power of a data visualisation lies in its appearance, as humans are easily attracted to images (this presents a problem of accessibility that ought to be addressed). Seeing is handled by the rear cortex, making it more effective than thinking. Data visualisation shifts the mental load from thinking. We can process visual elements such as proximity, similarity, enclosure, closure, continuity, and connection with great speed.
The heydays of data visualisation lie as far back as the stone age. Although historical cartography is often cited as its original form, data visualisation is said to have truly originated in statistical cave paintings that illustrated the success rates of hunts. Michael Florent Van Langren, a Flemish astronomer, prominently created the first recognised printed visualisation. Van Langren chose against the depiction of data in table form, as it would have been suitable at the time. Later, graphs started to be used to convey geologic, economic, and medical data — many forms of data representation that arose during this time are still in use today. Data visualisation was used in tracking the 1854 London epidemic and later found its utility within the Russian army, where frostbite fatalities were mapped against temperature. The industrial age became the golden era of statistical graphing, as social planning, medicine, military, industrialisation, commerce, and transportation meant larger datasets.
The 20th century saw a halt to data visualisation. Statisticians were increasingly concerned with its inaccuracy. However, around this time, visualisations became used in books and publications, gaining popular awareness. The 20th century also brought forward the discipline of psychology. The study of human perception greatly refined its methods and practices. Computer processing can be see a revival to the medium, as technology gave statisticians the ability too quickly and efficiently represent large data sets. Around this time, data visualisation became tied to innovation in computers, and remains to be co–existent with technology to date. Computers have democratised the medium, opening it up for non–specialists. With the introduction of affordable spreadsheet programs such as Microsoft Excel, technology has become powerful and accessible enough to democratised data visualisation for those that possess a computer. Graphs and statistical data representations didn’t have to be hand–drawn anymore.
„Given today’s Big Data environment, we can reasonably expect that at some point in the near future almost unlimited data collection, storage, and retrieval will be available at almost no cost, and that almost infinite records of all sorts of events may be indefinitely and indiscriminately kept and accrued, without the need for any systematic (or “scientific”) selection, sorting, or classification whatsoever.“
— The Alternative Science of Computation, Mario Carpo
Who is to design data? As creatives, scientists and business people compete for authority, automation is well underway. Giving control to an algorithm might seem like further democratisation — no more skills will be required to create a visualisation, making the medium as approachable as ever. Nonetheless, one must consider that a visualisation designer is not only versed in the technical underpinnings of his or her discipline but has a deep understanding of the medium’s audience. Social skills are a crucial part of the craft, and algorithms are unable to replicate the concept of empathy. We should, therefore, be leveraging the social skills of designers, rather than automating them, as a loss of said might come at great cost to how we communicate.
Data Visualization studios are often established design agencies, sometimes go by the term „experience design,“ yet seldom associate themselves with the craft of data visualisation — a word which might dismantle the mystery studios cherish to create around their work. Corporations are interested in roaring interactive installations, for it is cultural good and progressive marketing at once. As with any art form that prioritises the satisfaction of a business, its agency can be questioned. There are little forms in which art and business peacefully co–exist, as the product of an art piece generally holds little economic value. Deep, as opposed to shallow mainstream art, requires an understanding of its context and effort to consume. With media art, though, the technology becomes crucial to the piece itself. Artists depend on new technology, and tech companies see the chance for somewhat futuristic appearing branding. Media art often occupies an unusual co–existence with business goals. In the form of the art studios such as RNDR, Universal Everything, and Hey Hush, however, this observation holds little ground, as they supply a product which’s motive lies primarily in the satisfaction of a client. It is therefore applied, and perhaps should be categorised as design.
Though the medium in itself can be argued to be less expressive when compared to other forms of liberal arts, as it intends to simplify the existent. Yet, isn’t that what art generally does? Warhol, by example, made a statement about pop culture by replicating an existing image of Marilyn Monroe. Arguably, the photograph, as well as the condition of society he was seeking to critique, were pre–existent. His work is a literal visualisation of sorts — a simplification, one might argue. How does this differ from, let’s say, a visualisation that maps pollution to income per capita? Both works seek to uncover conditions too broad and widespread for the individual to easily identify. Both intend to educate and alert the viewer. By nature, a data visualisation only allows for a single interpretation, leaving little to no room for personal interpretation. Warhol’s prints, however, receive their political validity only when placed in context. They still exist without it, albeit in a different means, often individual to the viewer. The visualisation, on the contrary, is meaningless without context. Businesses seemingly appear to be more interested in the futuristic aesthetic that artists supply, and generally find less use of the values implied within their works.
Although this essay has established a critical point on the validity of data art. However, it is creatives that are likely to answer the questions that have arisen within the medium. Data visualisations are a necessity in order for us to understand the information we build or lives on. As we have established, it is the visual elements which give visualisations their effectiveness. Who is more versed in combining visuals and human understanding than designers?
All historical facts are taken from Dundas’s “A Brief History of Data Visualization”
Epstein, Robert. Your Brain Does Not Process Information and It Is Not a Computer — Robert Epstein: Aeon Essays. Aeon, 18 May 2016, https://aeon.co/essays/your-brain-does-not-process-information-and-it-is-not-a-computer.
Luo, Liqun. “Why Is the Human Brain So Efficient? — Issue 59: Connections.” Nautilus, 12 Apr. 2018, http://nautil.us/issue/59/connections/why-is-the-human-brain-so-efficient.
Enrico, Bertini, narrator. Stefaner, Moritz, co–narrator. Padilla, Lace, guest. “Cognitive Science for Data Visualization with Lace Padilla.” Data Stories, episode 148. https://datastori.es/148-cognitive-science-for-data-visualization-with-lace-padilla/.
Hazell, Jon. “A Brief History of Data Visualization.” Dundas BI Software — Dundas, 25 Sept. 2019, https://www.dundas.com/resources/dundas-data-visualization-blog/a-brief-history-of-data-visualization.
Carpo, Mario. Artificial Labor — The Alternative Science of Computation. e–Flux, https://www.e-flux.com/architecture/artificial-labor/142274/the-alternative-science-of-computation/.
Tateosian, L. G., Healey, C. G., and Enns, J. T. “Engaging Viewers Through Nonphotorealistic Visualizations.” In Proceedings Fifth International Symposium on Non-Photorealistic Animation and Rendering 2007 (San Diego, California, 2007), pp. 93–102.
“5 Psychology Studies Show How People Perceive Visual Information.” Piktochart, 10 Nov. 2019, https://piktochart.com/blog/5-psychology-studies-that-tell-us-how-people-perceive-visual-information/.
“Data Visualization for Human Perception.” The Interaction Design Foundation, https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/data-visualization-for-human-perception.