Correlation is an observable phenomenon. You can measure it. You can act on those measurements. On its own, it can be useful.
However, if all you have is a correlation, you do not have any guarantee that a change you make will actually have an effect (see the famous graphs tying the rise of iPhones to overseas slavery and such). It just shows that there is a correlation there, and if you tweak the environment (by acting), that correlation may still be there.
However, this is a very subtle approach. In many scenarios we want to have a less subtle tool: causality. Causality is a correlation combined with a claim that if you tweak your environment by acting in one way or another, one should expect the correlation to still be there. This allows for longer term planning, such as the chaining of 20 or 50 causal events in a row to identify a useful outcome. Doing so with 20 or 50 correlations often leaves a very fuzzy and murky result.
As an example of how they have been useful in the past, consider western science vs. Traditional Chinese Medicine (TCM). Western science focuses primarily on "Develop a theory, isolate a test which can demonstrate the theory, run the test and document the results." This starts with "develop a theory," which is highly tied to causality. TCM spun it around, starting with "devise a test which may provide useful results, run the test, identify correlations in the answer." The focus is more on correlations.
Nowdays westerners tend to prefer to think almost entirely in causality terms, so the value of studying correlation is harder to spy. However, we find it lurking in every corner of our life. And never forget that even in western science, correlations are an important tool for identifying which theories are worth exploring!