You already made a faulty assumption — that we're interested in "classifying the data" in the first …

I'm really not clear on why you're arguing against this. A proper data warehouse tackles the known unknowns, i.e. supervised learning. But you can glean new insights using unsupervised learning, like the textbook example of Target knowing a woman is pregnant based on sales data. https://www.forbes.com/sites/kashmirhill/2012/02/16/how-targ...

I might be misunderstanding your point, but there's use cases that have repeatedly come up for me in multiple businesses, below being some examples, without getting too specific: - identify latent features of customers via their behavioral data, to be used for profiling customers or recommending products to them - within a large amount of customer behavioral data, identify potentially fraudulent behavior - identify causes of seasonality (e.g. temporal patterns) in the data in order to improve forecasting (sales, traffic, whatever) In those cases part of the investigation is to initially take a hands-off (unsupervised) approach, so that we can compare our initial top-down hypotheses with actual patterns in the data. In both of those cases there's considerable (and sometimes adversarial) noise in the data.

> You already made a faulty assumption — that we're interested in "classifying the data" in the first place. It's not clear what your point is. If you're not interested in the predictions that tree-based models provide, do not use tree-based models on your tabular data. A predictive model and a SQL query are not the same thing.