Data cleaning is crucial for computer vision (CV) and machine learning (ML) projects to ensure model accuracy and efficiency, as unclean data can lead to costly and time-consuming errors. The process involves addressing issues such as duplicate entries, corrupted files, and inappropriate brightness levels in image and video datasets. Manual data cleaning is labor-intensive and impractical for large datasets, so automation tools like Encord Active are recommended to streamline the process. These tools help identify and rectify data anomalies, prioritize high-value data for labeling, and improve model accuracy, ultimately saving time and resources. Ensuring clean data before annotation and model training is essential to avoid poor model performance and wasted resources, emphasizing the importance of early-stage data quality assurance in successful ML projects.