Visualizations are created to make data more legible. They are intended to give us a neutral portrait, so to speak, of how collections of data relate to each other. In so doing, they make information accessible to us that would otherwise be obscured by its scale in a manner that is easily comprehended. Data is presented to us exactly for what it is so that it may foster the communication of information through the recognition of connections or relationships. This method of attempting to show data in an unadulterated, albeit creative way sees data more as its subject matter than its raw material. As laudable as this effort is, data as representation does not have to be the only way visualization is approached. Nor should a traditional visualization ever necessarily be perceived as the full picture. It should always be understood that there is an elusive, human, element, whether knowledge or otherwise, embedded in what is being communicated.
For artists such as Nathalie Miebach and Stefanie Posavec, the notion of visualization becomes more broadly defined and expressive. Both women spoke at this year’s Eyeo Festival and during their talks expounded upon and expanded the definition of data visualization proper. Firstly, for each the ability to work with their hands or to be able to tactilely interact with something visualized was very important.
For Miebach the inability to touch a visualization or to fully explore it with the contours of her hand made it difficult to truly comprehend. Posavec acknowledged there were different emotions associated with hand-making something and making something with a machine. While it might seem odd to focus on the hands when talking about visualizations it is important to understand the approach to it as modeling an object or design out of a raw material rather than to merely attempt to show it. The hand or body as a human experience that is something that can be lost in the flatness of a digital image, interactive or otherwise.
Take Miebach’s sculptures for example. She uses as her subject matter weather data from various environments and histories. She then either translates it into music or into colorfully elaborate weaved sculptures. For the sculptor cum visualist there is a subjective appeal to how she generates her creations. Whereas typical visualizations are “’didactic” in how they present data she calls her sculptures ”poetic”. She takes joy in the ability to walk around and explore the sculptures rather than sacrifice that dimensionality to the computer screen. For her it’s as important to foster an experience with the data as it is to discover new connections. Instead, in the same way folk stories preserve history, she creates narratives that contain traces of information. During her talk at Eyeo she asked whether fact and fiction could coexist and whether information becomes fictional by blending them together. The expression data in service of telling story becomes tantamount to their presentation. Like an abstract painting that does not come right out and say what its about but instead provides parameters for interpretation, her sculptures turn information into a panoply of meaning.
By that same token, Stefanie Posavec takes a similar, yet opposite approach. She uses fiction to generate data instead of the other way around. Using novels such as Kerouac’s On the Road she employs what she calls “data illustration” to trace patterns in the writing. By personally exploring the texts, she ‘visualizes’ styles and themes that reveal themselves to her within the immanent space of the book. The content of the book intermingles with her own personal traversal of the text to generate a new way of generating meaning from the ‘data’ that is already there. A new way of reading then begets a series of colorful illustrations that document her experience.
At Eyeo she characterized data as a lens for which to see a subject from an entirely new angle. The angle becomes primary over the data as a tool to see or as she calls it, a “souvenir of human engagement.” Posavec is then able to navigate a text, such as The Origin of Species, using data to discover design solutions, as she says, where informational insights aren’t the main purpose of the visualization. Taking the edits and updates between different editions of Darwin’s famous text she generates imagery that is aesthetically related to the subject matter in the form of botanically and organically inspired abstract images.
In both cases, data is not the primary focus of what is being visualized but springboard into something not as scientifically well-wrought but on the contrary is much more human and intangible. They are about not just seeing in a new way, but also creating new objects out of what already exists perhaps in contrast to the character of the so-called New Aesthetic. They are not satisfied with simply foisting a singular means of seeing the world upon us but offering something more shifting and elusive. They are bodily insofar that Miebach’s sculptures can be touched and walked around and Posavec’s designs are generated out of the physical effort of drawing them out over time. We don’t just look and see an image but something that we cannot immediately appertain and qualify. For both artists there is a kind of meaning the data can generate but that isn’t necessarily in the data itself. Data visualization is already in some cases an abstract enterprise in how the data is presented. However, in the same way that representational art sought to imitate the appearance of something that exists in real life, so too do representational visualizations. A standard visualization practice typically involves taking a large amount of data that is incomprehensible to an individual on a macro level and presenting it in such away that it is both visually appealing and legible. Often the former effort is an extension of the latter wherein an appeal to aesthetic sensibilities generates an interest in the data that is being showcased. In other cases it is a matter of finding the visual design that most clearly presents the data. In contrast, the data expressionism of Miebach and Posavec doesn’t attempt to neutrally visualize the data they are using. And whereas data representations refer to themselves insofar that they are visualizing their own raw material as subject matter, data expressionism uses data more as a starting point to suggest something that is indefinable and ambiguous, yet still truthful.
Representationalist visualization is all about pattern recognition and stopping at those patterns as enough to generate understanding. However, there will always be a danger that those patterns subsume what they are intended to represent on a superficial or limited level. Miebach and Posavec remind us that as important as data is for certain ends we cannot forget what could potentially exist beyond the mere image in the form of human experience.