While evidence of "graphical representation of quantitative data" dates back to the eighteenth century, it's hard to deny that the computer completely changed and, arguably, revolutionized those representations. Visualization practice lends itself, albeit in different forms, to every discipline—however, the sciences have held a stronghold on the field, producing pragmatic (and not necessarily aesthetically-pleasing) visualizations. The visualization, instead, was a tool for a practitioner in the field, like a doctor using a visualization of the human body for diagnosis. Until recently, the "cultural sphere" outside that was restricted to 2D charts and occasional 3D models, and not much more.
I appreciate how concrete these sections explain topics. I find definitional discussions in visualization literature refreshing because it adds more stable foundation to this rapidly expanding field. As for the answers to those large questions posed at the end of the first essay, my first reaction is that its the responsibility of the data visualization expert to make or advise which mappings are suited for each project. I agree that these questions are now as quintessential as older and more traditional ones, drastically shaping how a user consumes information.
Taking a step back and assessing representational art holistically, mapping with software can link old media to new media in completely new and not previously possible ways, creating a new form called meta-media. Meta-media preserves that nature of the old media while representing it as a new experience for the audience, simultaneously highlighting the software's important role in the mapping process.
As seen through provided examples by John Simon, Lisa Jevbratt, and Alex Galloway, data visualization mappings aim to take patternless and chaotic data and package it in a patterned and organized visual, ready to be understood and consumed. It maps from the concrete to the abstract back to the concrete.
I've read a lot of interesting literature about the role of the sublime in data visualization, and I am both equally startled and excited to see it discussed in the last part of this essay. From this previous literature, I hold the sublime as an important, concrete term to use when talking about data visualization's relationship with art. However, I argue against what the author writes, that data visualization is concerned only with the anti-sublime. I see sublime as a sliding scale, adjusted depending on what type of data visualization is being practiced. I think pragmatic data visualization deals with the anti-sublime, artistic data visualization deals with the sublime, and information visualization edges closer to the middle.