Raza Habib, co-founder and CEO of Humanloop, emphasizes the significance of a data-first approach in machine learning over merely focusing on algorithm improvements. He highlights that enhancing the quality of datasets can lead to superior AI performance, as demonstrated in a case study where improving the dataset for a computer vision system increased its accuracy by 16%, outperforming human abilities. He identifies common "data bugs" such as misannotations and inconsistent labeling guidelines, which can hinder model training. Habib advocates for tools that aid in detecting and correcting these issues, such as data cleaning tools, weak labeling, and active learning, to improve data quality and address class imbalances. The article underscores the collaborative potential of this approach, involving non-technical subject-matter experts in the AI model training process, facilitating better teamwork and more effective machine learning outcomes.