In Kieran Healy’s article, he discussed three obvious problems of data visualization tend to be aesthetic, substantive, and perceptual. An understandable graph always can decode dataset, visualize each variable rationally, and build a well communication to people.
The “Monstrous Costs” by Nigel Holmes is an eye-catching graph in graphic design aspect, but it also exposes many unnecessary design features. As Edward Tufte mentioned, “[a wonderful graph should] avoid content-free decoration, including chartjunk” (Tufte, 1983, o.177). The reasons is data visualization is more focus on the functionality (how to decode dataset) rather than draw a beautiful graph. Sometimes, subjective aesthetic sense can make us to create a graphic without the readability of information or data. The goal of data visualization is help people to read and analyze dataset, and we should be aware of any meaningless visual element caused by our bad taste.
In the following discussion, Healy explained how bad data could mislead people even it has a well-designed graph. For example, any two unrelated variables (or does not have a reasonable relationship) on x-axis and y-axis can lead a graph to failure in the beginning. Meanwhile, a good infographic with wrong dataset will pass an inaccurate information to people. Therefore, we should to filtrate all the variables, decide which one could be an effective solution, and it is the same important as our aesthetics.
Between data and aesthetics, perception can affect the way of how to visualize data on the psychological level. In the section 1.2.3., Healy gave us an example by the recent version of Microsoft Excel, the 3-D bar chart expressed its aesthetics and limited redundant decorations at the same time. The whole visual system follows the rule of how human read, analyze, and understand data by their minds. As the deeper discussion, our visual perception is built on three elements which are edge, contrast, and color. On one hand, those elements brought more possibilities and options to represent a bigger dataset visually. However, it could make people to misunderstand data as well.
In my opinion, the only standard of data visualization is not existing because we all have different ways of thinking. A high valued graph is based the previous experience, and its design depends on each scenario and who will be the user.