Taste changes， the criteria for good and bad design also changed as well. So when comparing with bad data and bad design, bad design is worse.
Speaking of the Data junk issues. The author here claimed that “Viewers do not find them more easily interpretable, but they do remember them more easily and also seem to find them more enjoyable to look at. They also associate them more directly with value judgments, as opposed to just trying to get information across.
When you are facing with redundant data which makes you confused, you would like to see a precise and clear graph that helps you to understand the inner trend of the dataset. Then data visualization is here to help you extrapolate through the dataset.
And sometimes bad taste can have a negative impact on data communicating. there is no proof that the embellishment of data visuals can offer more necessary information. It do contains more information that may show the trend of data, but the simple design of data can also have that kind of outcome.
I should mention about working efficiency, during the experiment, the author claimed that are often more easily recalled than their plainer alternatives .” Even if I admit the positive effect here, does it mean that we should spend more time in embellishing the data to make it more attractive and memorable or we should stick to simple and precise design that have similar effects but we can have more efficiency. No one graph can display the full story that lives in a set of data. When taking in a real world scenario, we always get trapped by a word called KPI. Maybe in that case, working efficiency values more than the interesting design of data.
Personally speaking, I found that design requires professional training , if only a professional designer can use data junk to express data, then does it mean that, for us, it is safer to use the plain and boring design. At least it can offer you essential information, they are not rich, but at least covered the required piece.