This seems like a fairly useful tool, but I'd be a bit cautious - the tradition of poring over a carefully collected and curated data set using tools you understand the strengths and weaknesses of shouldn't be lightly tossed aside. That process can help researchers spot unusual anomolies that lead to novel discoveries, while an automated tool might just discard all outliers.

Incidentally, the far more concerning issue is the use of approaches like this to generate data which opens the doors to a plague of hard-to-detect scientific fraud. In that past, many such high-visibility fraudulent efforts have been detected because the fraudsters duplicated data (or reversibly processed old data in some manner) that was spotted by others in the field, e.g.

https://en.wikipedia.org/wiki/Sch%C3%B6n_scandal

Often these fraudulent productions are inspired by the desire to be first to publish, a situation in which everyone thinks they know how a system works but they're all rushing to get credit (and hence Nobel Prizes and patents etc.) by generating the data from a 'successful experiment' before anyone else can.