Imagine you have a dataset of people who have cancer.
You have information about their age, physique, diagnosis, treatments, and results.
Using this data, you want to prescribe a set of treatments for a new patient, P.
Obviously, if there is someone in the dataset that has very similar traits as P and had a positive result with their treatments, you could prescribe the same set of treatments. However, this is incredibly unlikely and becomes more infeasible as more information about P is observed (e.g. Has brown hair and hates pasta).
A better option is to cluster the dataset into groups that have positive outcomes for treatment results. For example, perhaps patients with lung cancer who smoke and are given treatment A do better than patients with lung cancer who didn't smoke and are given the same treatment A. These patients should then be divided based on this outcome.
Once these different clusters are found, patient P can be evaluated against each of the clusters and a set of treatments can be prescribed (e.g. Most of the treatments from cluster A, but 1 treatment from cluster B).
Unsupervised learning is the method of finding these clusters, which helps find structure to the data to better answer questions.