GIGO Garbage In, Garbage Out
Computer science fundamental: bad input → bad output, no matter how smart the algorithm is.
Classic ML: 90% of "the model performed badly" is bad training data. Classic analytics: a pretty dashboard on corrupt data only misleads. The driving force behind the data quality movement, data observability tools, "data is the new code".