I agree, and would add: data cleaning's importance to the quality of the result is also often underemphasized compared to the much bigger focus on the quality o

I agree, and would add: data cleaning's importance to the quality of the result is also often underemphasized compared to the much bigger focus on the quality of the algorithms. A single bad decision on data cleaning can have a large effect on the end result (in many cases, more than choosing between algorithms, assuming you pick some vaguely reasonable algorithm). Especially any choice that ends up producing non-random effects, like deduplicating things in a way that ends up biased: it's common that missed duplicates in an automatic deduplication process aren't randomly distributed. Or a scraping process that ends up with biased samples. You can correct for these kinds of things in various ways (e.g. incorporating an estimate of the bias in a statistical model), but people who don't consider data cleaning a "real" part of the whole statistical modeling pipeline in the first place usually don't.