So true. Missing from university textbooks is the fact that real world data is often dirty as hell, inconsistent, distorted, biased, incomplete, and/or just plain invalid (measured the wrong source or too imprecisely to be useful).

Unless your DS group is big enough to warrant hiring data engineers / cleaners, as a data scientist it'll be your job not only to eventually choose the algorithm, but foremost, to confirm that the data is sufficient to serve the intended purpose of mining it, ideally before you waste a lot of time curating it or paying for a raw data dump you can't use.