I've frequently found, over a few decades, that numerical systems are cyclically 'corrected' until results and performance match prior expectations.

There are often more errors. Sometimes the actual results are wildly different in reality to what a model expects .. but the data treatment has been bug hunted until it does what was expected .. and then attention fades away.

Or the company just changes the definition of success, so that the metrics (that used to be bad last quarter) are suddenly good