Most people focus heavily on model improvements while treating data quality as a secondary concern.
They spend hours tuning hyperparameters, testing new architectures, and following the latest research, only to see performance stall at the same frustrating accuracy ceiling. More training rarely fixes it. More augmentation often does not either. Even swapping one strong architecture for another may not change much.