Machine Learning (ML) models are only as good as the datasets they are trained on. The quality of the dataset plays a pivotal role in determining the performance and reliability of the resulting models. However, achieving high-quality datasets is not a one-time task; it requires an iterative process of understanding, assessing, and refining data to enhance model performance continually. This presentation delves into the critical role of iteratively assessing dataset quality concerning model performance in the realm of Machine Learning focusing on the ADAS/AD use cases.