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Information Visualization: Defining Audience

What can we learn from “An Inconvenient Truth” about how to tell a
complicated story with a chart or graph? Well, that it’s often a very difficult task.

A recent post addressed some basic guidelines common to information visualization design and evaluations. I’d now like to bring up a question related to both these tasks – what effect does type of audience have on these guidelines? Often, information visualization books, such as Card’s excellent Using Vision to Think, which covers numerous aspects of data viz, do not give much treatment to questions of the designated perceiver. On a general level, it may seem obvious – a visualization targeting clients should be simpler to process than one designed for analysts who have more time to spend visually exploring the data. But this doesn’t do justice to some of the finer target audience-based distinctions, which can lead to debates over guidelines and best practices if unmentioned.

In checking around for online discussions around this, while there are plenty of blogs devoted to info viz, I found that most (like information aesthetics, or meryl’s) seem to focus primarily on supplying a survey of novel and interesting visualizations, without going too far into evaluating them empirically, or considering the design process.

One online resource that did touch on this aspect of design is The Adobe Design Center Think Tank’s article on
the controversial visualization of the temp/carbon monoxide relationship in Al Gore’s movie “An Inconvenient Truth”:

Relationship between temp/carbon monoxide over 1000 yrs.

The controversy was around labeling of the y-axis, and that a perceiver can’t tell whether warmer temperatures precede or follow the rises in levels of carbon dioxide. According the graph’s designers Duarte Design (who helped create all the graphs in the movie, but failed to comment on this particular design), “In general, you want to keep the visuals minimal and eliminate background noise to emphasize your point.” The Adobe article goes on to point out that “dumbing down” or simplifying the graphics can make your audience unlikely to agree with the pattern you’re trying to enforce through the graph, citing Edward Tufte’s famous observation that less is often just less when it comes to visualizing information, as it sacrifices nuance and thus credibility. Isn’t science “complicated”?, people tend to think, and its difficult to convince them otherwise.

Adobe ends the article with a reference to how combined narrative / visulalizations are becoming “the most common way of presenting information for businesses, academics, and the military.” To be effective, dramatic results must be carefully introduced; the realization must be gradual to make the visualization seem accurate and believable.

To me, this begins to touch on some features that can distinguish visualizations designed for those familiar with a topic but not immersed in the data, such as clients coming to a company with an analysis need, versus those geared toward the analysts themselves.

Its a somewhat debatable issue, in my opinion. I’ve met people (including a company I designed a viz for) that ultimately decided if they were to put a visualization in front of clients, it had to be simplistic. Myself (perhaps as an analyst?) am not satisfied with overly-simplistic graphs in any context; its the data that interests me.

I thought it would be a fun exercise to try and categorize real distinguishing features of viz’s for each audience type, and some that are shared:

Client Audience

  • prettier – (custom color schemes?)
  • make use of convention as a default
  • metaphors/branding interwoven in design
  • delivered in narrative context

Analyst Audience

  • multiple views
  • access to info on data mapping
  • interactivity
  • access to much granularity

Shared Characteristics

  • maximize automatic processing
  • use easily distinguishable colors – (beward of red/green color blindness, which is relatively common)
  • minimize use of text by default
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