State-of-the-art 3D classification models are showing saturating performance on the popular ModelNet40 benchmark. We investigate possible causes for the remaining mistakes and find various data-related issues. In summary, our goal is 1) to give suggestions for future dataset creation in 3D deep learning and 2) to provide ground-truth information on mistakes for evaluation of (future) automated data cleaning methods.