Last week I shared a number of recommendations of original, well-founded contemporary projects to compliment comments by Michelle Borkin and Nick Matzke on data and visualization. I had intended to share some more this week but I became preoccupied with a debate that is taking place right now—hopefully I can provide a few points of entry into this dialog. Given that this post is about meandering discourse, please note the detail from Warren Sack's prescient Conversation Map (2000) on the left.
Over the last few weeks a boisterous scrum has broken out between several notable thinkers and practitioners within the field of information visualization. A large portion of this conversation revolves around the role of aesthetics and function within the discipline but it also addresses narrative and best practice. In his recent Information Visualization Manifesto, Manuel Lima stated that the
same dataset can originate two parallel projects, respectively in Information Visualization and Information Art. However, it's important to bear in mind that the context, audience and goals of each resulting project are intrinsically distinct.
This assertion is essentially correct but I feel these categories are too narrow to use in scrutinizing the vast majority of information-rich graphics that we now encounter on a daily basis. We are witnessing a minor gold rush in the humanities as scholars in numerous fields are flocking to data visualization to augment their work. At the same time (and I've heard scholars lament this myself) researchers whose work is completely based off of quantitative research are looking to sex up the appearance of their graphics to increase the marketability of their work—generic scatterplots just don't cut it anymore. Beyond this, if you step away from the ivory tower and glossy design monographs and peruse almost any media source there is an abundance of flowcharts, infographics, mashups, real-time visualizations and tools for mapping social connectivity that further drive our appetite for information-rich visual communication. While I agree with Lima that there is a need for an increasingly specific vocabulary for analyzing information design ("art" and "visualization" alike) within the expert community, I think a more pressing issue is to focus on whether the lay users of visualization know how to assess the merit (or lack thereof) of the work they are examining. I'd argue that if there is any definition that needs to be splayed out on the dissection table it is that of visual literacy.
Earlier this year Nathan Yau stated the following about an ideal relationship that researchers might have with their datasets:
If you really want to learn about a large dataset, visualization is only part of the answer. It's an exploratory process. You create a graph. You create a whole bunch of graphs. Notice anything interesting? Okay, let's look over there. This process is called exploratory data analysis, coined by famed statistician John Tukey back in the 1970s. Too often we settle on a particular graphic because it looks pretty, or worse, it helps prove our point. We get blinded by outside motivations, that we forget to listen and look at what else the data have to say.
Nathan's point definitely applies to the researcher's workflow, but how does it relate to us as "readers"? What knowledge is required for understanding and analyzing information visualization (in all of its guises) and how do we acquire and bolster these skills? I believe this is where careful consideration is most needed.
Those interested in digging further into this discussion should investigate the following links. I should also mention that I've only really zoomed in on a portion of Lima's (rather expansive) argument—please read his entire original document. Be warned that in browsing the links below (specifically the comments) you may encounter strange analogies about furniture assembly and prickley-goo.
- Manuel Lima's Information Visualization Manifesto and subsequent observations
- Robert Kosara's follow-up
- Tom Carden weighs in (concisely)
- Log in to post comments