The other big catch is randomness, which is often not understood by neophytes. If you try to find relations in sufficiently large data set, you're bound to find some that are "caused" by randomness. Tools like p-values are of little help when you fish for many relations (and not just one in particular).
Randomness can be a curse, but can also be a blessing when introduced as in the random subspace methods. This again abstracts to understanding your business needs and whether the results encountered make sense given the features' [absence of] independence. An API giving you a wide choice of algorithms will still rely on you to run something like ICA as a pre-processing step to identify this statistically independent randomness.