> Using the strengths of the human vision system to get a rough idea of what the typical data looks like and the frequency and character of outliers isn't dumping the job of exploratory data analysis onto the reader, it's how the job actually gets done in the first place.

Yup this is a good summary of the intent, we also have to remember that the eigenfaces dataset is a very clean/toy data example. Real datasets never look this good, and just going straight to an eigendecomp or PCA isn't informative without first taking a look at things. Often you may want to do something other than an eigendecomp or PCA, get an idea of your data first and then think about what to do to it.