Sometimes, the only thing I find by doing exploration is that a particular dataset is absolute garbage and shouldn't be used for any purpose. The only way I find stuff like that out is if I explore the dataset.
People are not paid to find "something", they are paid to find specific things.
Hence, the following makes even less sense that TFA:
>This something might not be useful to an "end user," but it is almost always useful and necessary.
Analysts who only find the specific thing and end their work on that are a dime a dozen, and need to be micromanaged. Good analysts will find all the other interesting stuff on their own and inform the business about it. Those good analysts are the explorers, and banning those people form exploring during training seems like an effective way to take talented budding analysts and turn them into mediocre ones.
What he's going for is to have the students explore the data and get insights into it, and then actually use those insights and data to create their projects.
And I think that is a really good idea.
It reminds me of a thread on MathOverflow (the StackExchange for professional mathematicians) asking for proofs without words (which tends to fall in the category of "visual" proofs: the second most-voted answer is "because I think proof by picture is potentially dangerous, I'll present a link to the standard proof that 32.5 = 31.5". Link: https://mathoverflow.net/a/17347
> think it is just as easy to introduce some kind of logical gap in a written proof as in a graphical one.
Exploration is absolutely one of the key goals of data visualization. Tukey's insight was two-fold: First, that statistics puts too much emphasis on confirmation but not enough on systematic exploration of a data set. Too often researchers do not understand their data. They bring their own biases and preconceived notions, where they should be listening to the data.
Second, too often is visualization mistaken for confirmation. A curious pattern or an outlier may just be an artifact of the layout algorithm. The "find" part of visualization should happen through mathematical insight. Graphics can at best describe the underlying mathematical reality but no more. One cannot strictly speaking "find" anything, only form intuitions or illustrate already proven insights.
Perhaps the author's difficulty in evaluating his students work lies not in the exploration part of their assignments, but in his own pedagogic emphasis on "tools," "frameworks," and "users." None of those things are relevant to data visualization as such. They might be goals for a business built around data visualization (to produce tools, or to identify user needs). A university should offer more than job training. Those interested in users and in "what one gets paid for" would do better in an internship or at a more narrowly technical trade school.
I don't know how "finding" is any more of a goal for data visualization than "exploring." Data visualizations tell stories. They often support the first and the last step in data analysis: the exploratory phase and the presentation of findings. They are inherently subjective, evocative, concise, artful.