Common uses are clustering customers into segments, detecting anomalies such as fraud and reducing data to fewer dimensions for visualization. Self-supervised learning — predicting hidden parts of the data, like the next word — is a close relative and powers language model pretraining.
Because there is no right answer to compare with, judging results usually requires domain expertise.