In my last blog post I discussed “The State of the digital Humanities: a report and a critique” by Alan Liu. In his article, Liu argues that the digital humanities is missing what he calls “Data aesthetics” (27). The list, line or bar graphs and tag clouds, Liu contends, reflect “the near-total imaginative poverty of the field in crafting an aesthetics of data” (27). Despite Liu’s claims that there is more focus in the digital humanities on metadata than “the look-and-feel of data” (27), however, the “availability and democratization of data” has brought about a vast increase and popular demand for data and information visualization (Lang n.pag.).
Data visualization can be defined as “the use of computer-supported, interactive, visual representations of data to amplify cognition” (Card 1). Data visualisation, however, not only amplifies cognition, but it also helps one deal with the “Data glut (McCandless),” by reducing “the mental load” on the user (Cawthon & Moere 2). David McCandless maintains that using our eyes is one way of coping with information overload because sight is the fastest of our senses. Eyesight has the same bandwidth as a computer network. Using aesthetic visualisations, therefore, facilitates knowledge compression and increases the speed of knowledge digestion because it stimulates the visual cortex of the user’s brain. In a sense, data visualisations resemble works of art to infiltrate the mind of the user. As McCandless notes, visualisation creates a new language that alters our perceptions. The interactive component of data visualisation allows us to participate as “data detectives” in the search for hidden clues exposed by the visualisation process. As Jer Thorp observes, visualisation makes data human by putting it in a human context. When data is sewn into the fabric of the real world, it gains meaning and weight. Realising this fact alters our dialogue with the information we are attempting to transform into knowledge.
Having said that, however, data visualisation is not a fool proof endeavour because a picture is not worth a thousand words if the viewer cannot decipher it. Nick Cawthon and Andrew Vande Moere observe, “the notion of beauty is not a normative element”. Perception of aesthetics is highly subjective. Therefore, an object cannot be viewed in isolation to its social environment and socio-cultural context because of the cultural and cross-cultural differences in visual language interpretation. The pitfalls of visualisation, Bresciani and Epplen claim, are “due to the fact that the meaning of symbols and colours are not universal” (11). This is evident from the foreground-background preferential differences in art of Western and Asian cultures, the cultural differences in the meaning of the colours red and green, and the way some eastern countries display time in a right to left format. As well as the problem of cultural bias, both the data visualisation user and designer require visual literacy and previous knowledge and experience with interpreting graphical displays. The psychological and aesthetic restrictions of data visualisation can confuse the user if the inherent meaning of the visualisation is ambiguous, but ambiguity may have a positive effect by effectuating new insights through creative interpretation of graphical depictions of data.
Questions to consider:
(1): Is data visualisation an aesthetic or anaesthetic? Does it enliven or numb the mind of the user through the graphical representation of abstract data?
(2): Why is aesthetics an important factor in information visualisation?
(3): Is visualisation a technology, a science or art? Does it have aesthetic value or merely aesthetic pleasure? Does data visualisation dilute perceptions of art or broaden its conceptual boundaries? Does data visualisation have to be artistic to be effective?
(4): Data visualisation could be considered as an interdisciplinary subject? Do you think it flawlessly integrate science, art and design?
(5): Does data visualisation distract from the main goal of knowledge transfer? Is it more than a form of decorating or aestheticising dull data?
Bresciani, Sabrina, and Martin J. Epplen. “The Risks of Visualisation: A Classification of Disadvantages Associated with Graphic Representations of Information”. 2008. PDF.
Card, S. K., et al. Readings in Information Visualisation: Using Vision to Think. San Diego: Academic P, 1999. Print.
Cawthon, Nick, and Andrew Vande Moere. “Qualities of Perceived Aesthetic in Data Visualisation”. 2007. PDF.
Lang, Alexander. “Aesthetics in Information Visualisation”. PDF.
Liu, Alan. “The State of the Digital Humanities: A Report and a Critique”. SAGE 11.4 (2012): 8-41. Web.
McCandless, David. “The Beauty of Data Visualisation”. TED. 2010. Presentation.
Thorpe, Jer. “Make Data more Human”. TED. 2011.