Effective machine learning systems hinge on high-quality data, making data bugs a significant concern that engineers must address during development. These bugs can arise from issues like missing values, incorrect annotations, data inconsistencies, and corrupted or duplicated data, which can skew model training and evaluation. Conversely, having the right data is equally crucial, as a lack of representative data can lead to inadequate system performance, such as an autonomous driving model failing on roundabouts if such scenarios are underrepresented in the training data. To combat these challenges, implementing robust data tests is essential, ensuring that the datasets are both correct and comprehensive from the outset. Mature teams prioritize data quality testing early in their projects to prevent unnoticed data bugs from causing delays, emphasizing continual monitoring to maintain data integrity.