In my (admittedly short) experience as a data scientist, "solving the wrong problem"/"working on irrelevant things" and "inadequately cleaned/prepped training data" are vastly, overwhelmingly more common failure modes than "building the right thing with good data inputs but misunderstanding the algos." Probably more common by an order of magnitude or two.

Then again, maybe I'm just working at companies with problems that are amenable to easily-understood algos but have plenty of data-and-product-themed problems.