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.