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.
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.
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%.