Handling missing or incomplete data is a common challenge in almost every project. Techniques like imputation, filtering, and data augmentation can help, but the right approach depends on context. In visual data tasks, understanding the difference between computer vision vs image processing is important, since each approach handles data issues differently and impacts how missing or noisy inputs are managed.