I found this week’s reading to be quite tough – Manovich’s oblique, academic, and often philosophical language was at times quite hard for me to navigate. As someone who identifies more as a pragmatist than artist in his pursuit of data visualization, many of the examples and projects Manovich provided felt like intriguing sidetracks to my perspectives in my studies. While this perspective is undoubtedly valuable to these studies and my growth, as they serve to expand my notions of how software and data visualization contributes to the world of modern art, I’ll use one of the countless sports metaphors within American lexicon to say that this reading didn’t quite “hit home” for me.
Still, I did find some useful considerations in the discussions Manovich introduces in this piece that stuck with me. First was his conversation about the dimensions that we have available to represent in when “a data set is mapped to an image,” which Manovich uses as his definition of data visualization. Manovich brings up interesting questions around the choices and trade-offs we make to represent data that often has more than four dimensions in a mapping limited to four dimensions, since human experience exists in a 4D space (X,Y,Z, and time). We must grapple with the questions Manovich raises every time we want to represent data in a visual space – deciding which mapping to use, what dimensions to include, and how to map those dimensions to a visual characteristic determine what message these visualizations communicate and how our audience perceives that message. It is in these choices where our visualizations can become successful or deceiving.
I also found Manovich’s conversations around meta-media to be a useful exploration of software’s transforming effect on how we interact with media (although I found his definition of meta-media itself to lack clarity and be unnecessarily dense), specifically his explanation of software that preserve the core elements of an old media object while providing new ways to interact with and control that object. His example of Adobe Reader is particularly interesting in how it co-opts elements from interfaces of different media and applications to provide an intuitive navigable interface for that specific program.
Finally, when Manovich speaks to how data visualization brings us the possibility to map phenomena that were previously un-representable into “a representation whose scale is comparable to the scales of human perception and cognition” that fit within a single browser frame, I think he really strikes a nerve. Having an endless menu of mappings and aesthetic levers to pull provides an immense feeling of freedom and possibility (while also at times inducing the anxiety that comes with staring at a blank canvas), but also makes those choices seem arbitrary.
“By allowing us to map anything into anything else … computer media simultaneously makes all these choices appear random – unless the artist uses special strategies to motivate his or her choices”.
This point reiterates a piece of feedback I remember from my 7 Numbers project – the posters I made that reflected aspects of the content itself in my representations of that content (like the votes organized in a senate seat floorplan) were more successful than my representations that were abstracted so much that they appeared purely random (like the 500 stocks organized in a grid given one of two shapes). Manovich sums up my feelings on this subject perfectly, as he represents my continued focus on grounding visualizations in representations that are reflective of the content itself, while not always knowing the best way to do that:
“Maybe in a ‘good’ work of data art the mapping used must somehow relate to the content and context of data – although I am not sure how this would work in general.”
I’d also be interested to hear from Richard, Christian, and my fellow students what they interpreted as the definitions of some of the key terms Manovich used in this piece that went slightly over my head – specifically meta-media and sublime/anti-sublime.