In the realm of computer vision, data quality plays a pivotal role in determining the accuracy and reliability of models, as outlined in a guide that underscores its significance and explores strategies for enhancement. The guide delves into key attributes of high-quality data, such as accuracy, consistency, data diversity, relevance, and ethical considerations, and examines how these factors influence model performance, including improvements in accuracy, generalization capabilities, and robustness. It emphasizes the importance of balancing data quality and quantity and highlights the impact of label quality on model precision. Tools like Encord Active are showcased for their role in data curation, management, and annotation, facilitating the detection of outliers, ensuring ethical data collection, and reducing labeling costs by efficiently curating datasets. The text further distinguishes between data cleaning and preprocessing while advocating for data-centric approaches, such as active learning and semi-supervised learning, to refine data and improve model performance. Additionally, it addresses challenges like outlier detection, data imbalance, data drift, and problematic images, providing practical techniques and tools to manage these issues, ultimately aiming to optimize data quality for superior computer vision model outcomes.