This year’s edition of the annual Infographics Conference featured presentations that were loosely connected by the possibilities attributed to personal data and visual representations thereof. Though I took home many things from the conference, I was mainly triggered to think about the role of designers, organizations, and technologies in the process of ‘teasing out’ worthwhile information from data sets – how do personal, organizational, and technological aspects of data visualization shape our understanding of data?
Retaining the past through quantification and visualization
The main theme of the conference, dubbed ‘Homo Infographicus’, was nicely illustrated by the first two talks by Nicholas Feltron and Jack Medway. Feltron is known for his meticulous process of collecting and visualizing personal data (e.g. the streets he walks on, the amount of time he spends with people and how he chooses to do so, food eaten, moods, etc.). His yearly ‘Feltron Report’ features various visualizations of data collected over a particular period.
Medway alluded to the ‘nightmare of personal data’, which can be aligned with projects such as Feltron’s: you could quantify and visualize your life, but why the hell would you want to? At this point I was reminded of a quote from the character Fred Madison in David Lynch’s ‘Lost Highway’:
“I like to remember things my own way … how I remembered them, not necessarily the way they happened.”
Is an objective view on the past possible, or is one’s view of it always shaped by cognitive processes, emotional attachment, desire … and technologies? ‘Capturing’ the past takes place through the creation of indicators that, when collected, parsed, refined, and visualized, supposedly yield some kind of ‘objective’ view of ‘lost time’: retention through quantification and visualization. During Medway’s talk, I took Feltron’s beautiful work as a starting point to think more generally about the inscriptive effects of using technologies to quantify and visualize one’s personal histories. There is no objective view on the past, but rather a prism that both distorts and reveals.
Balancing ‘subjective’ interpretation and ‘objective’ data
Stefanie Posavec’s presentation featured similar concerns between the ‘objectivity’ of data sets and the ‘subjectivity’ that comes into play once data is visualized. Posavec defined her work as a process of ‘data illustration’, which is about the question how one can push objective data into the subjective sphere while still being truthful. During her talk, Posavec quoted the work of Bruno Murani:
A leaf is beautiful not because it is stylish but because it is natural, created in its exact form by its exact function. A designer tries to make an object as naturally as a tree puts forth a leaf. He does not smother his object with his own personal taste but tries to be objective. He Helps the object, if I may so put it, to make itself by its own proper means, so that a ventilator comes to have the shape of a ventilator … Each object takes on its own form. But of course this will not be fixed and final because techniques change, new materials are discovered, and with every innovation the problem arises again and the form of the object may change. (Bruno Murani – Design As Art)
The role of the designer is to work ‘with’ the material or tap into affordances of the data. In other words, the designer is a catalyst who does not so much project a definite meaning on a data set, but provokes a data set to speak on its own behalf. Discussions on craft have featured the theme of working with the potentialities of a particular material over and over again (e.g. Zhuangzi’s butcher, Gilbert Simondon’s brick maker, and Gilles Deleuze and Felix Guattari’s woodworker), and this is certainly a theme I’m exploring in papers I’m currently writing (and I aim to do so in the future as well).
The intersubjective aspects of data visualization
Rather than a ‘subjective’ interpretation of data, one could also emphasize the ‘intersubjective’ sphere of data visualization. Yael de Haan pointed out the organizational challenges of data visualization during her talk, and made clear that team members will always take their own expertise to the table. Such perspectives shape the process of developing data visualizations – and why not cherish the potential benefits of interdisciplinary work when possible?
Moritz Stefaner’s analysis of ‘selfies’ pointed to similar ‘intersubjective’ aspects of data visualization. Stefaner collected a large number of selfies from several cities and analyzed these images for similar features, such as tilting of the head, presence of glasses, mood, and so on. In the process, Stefaner came to the realization that there is no fixed data set, but that the data set develops while one explores it. In other words, allowing users to play with a data set reveals new ways to categorize, analyze, and search the data set – a process called ‘learning through playful interaction’. Stefaner concluded that data visualizations are great for making the complex simple and telling stories, but that this is no confined to the work of a single author.
Designing data visualization could be a process that embraces this multitude of perspectives, making data visualizations act as a ‘macroscope’ (a concept developed by Joël de Rosnay his 1979 book of the same name). Stefaner’s presentation can be seen as a call to arms: designers should develop new types of devices to deal with complexity. They should tell worlds, not stories.