Why this course?

Data visualization is ideally supposed to make complex data more accessible, understandable, and usable. I think this occurs for two main reasons: 1) the data is boring, but the author wants to highlight a particular message or 2) the data is very technical, but the author needs to present the information to a layperson.

Edward Tufte presents a great example of how engineers failed to clearly present technical data prior to the Space Shuttle Challenger accident. In summary, the engineers had thirteen technical charts and diagrams to present their case for why the launch should not occur. Their managers remained unconvinced about the chances of an issue because their presentation had technical data, but did not make the explicit visual connection between temperature and failure, such as the chart below that shows launches chronologically.

Example of a problematic chart that lacks a legend and clarity into the presentation of data.

Tufte criticized their charts and reconfigured a table to show clearer causality between temperature and failure (below). If the engineers had been able to give a clear and concise chart, then the message might have been that colder weather increases the chance for failure rather than that the engineers said a failure might occur without any compelling evidence to support their concerns.

Tufte’s suggested revision for the presentation of the same data.

The accident investigation found fundamental issues in the safety and mission assurance culture. In other words, these engineers had no way of reaching the highest levels of management directly to convey their message. They presented the information to their managers, who sent it up the management chain, which diluted the urgency of the message. Tufte also would add that an improved presentation might have been able to convince managers and allow the message to reach a decisionmaker.

This is one of my favorite examples for why careful thought and planning for visual presentation is important. However, I recognize most instances for data visualization do not have life or death consequences unless being “bored to death” counts. The data usually is exciting only to the researcher, so a visual presentation helps make the research more interesting to other people.

In addition, the various required research methods and statistics classes generally focus on learning methodology. The results get dumped into very basic charts or tables straight out of SPSS.

My personal goal for this class would be to use the opportunity to assess prior work to prepare for the practicums requirements. The general practicum will synthesize federally funded scientific research and public opinion. The in-depth practicum will be a case study of direct to consumer genetic testing and some public misconceptions around the science. I think the general practicum presents an opportunity to work with information that others may not find as interesting. The in-depth practicum has the potential for technical explanations to show why an ancestry test cannot reliably provide the neat percentages adding up to 100%.


2 thoughts on “Why this course?”

  1. My grandfather was one of the engineers that worked on the booster rockets for the Challenger so that’s a story I’m very familiar with.

    I’ve seen some people use The Cognitive Style of PowerPoint as an indictment of the software (and I think Tufte does more of this than I’d like). He discusses the Columbia Incident (rather than the Challenger) in this one. That being said this was my desktop background for a while.

    I like much of Tufte’s general ideas around simplicity and clarity but struggle with some of the work being understood by general audiences. It makes sense if you invest in it but it’s worth thinking hard about when that desire to work towards understanding exists in your audience.

    Can you give me a better idea of the type(s) of data and connections you’re trying to draw in the practicums? Do you have a particular argument planned out? Have you seen examples that are like what you want to do?

  2. It’s also worth thinking through the idea that data visualization might enable the expert to see or understand something they wouldn’t catch otherwise. This becomes especially true with massive data sets or especially complex relationships. I’m particular interested in data visualizations that enable people to manipulate the data to come to their own understandings or to try out hypothesis. You see that in things like GapMinder or Keshif and in some of the better Tableau examples.

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