Artificial intelligence (AI) applications rely heavily on the quality and management of data used to train the AI models. This chapter discusses the importance of data for dental AI, starting from the foundational equation of computer processing, AI algorithms, and data. It emphasizes the role of data quality and provides an introduction to dataset preparation, including annotations, partitioning, size considerations, sources, and potential biases. As the demand for data grows, and also the privacy concerns, the chapter introduces federated learning as a novel approach to AI algorithm training preserving data privacy. Central to this discussion is the adoption of the FAIR (Findable, Accesible, Interoperable and Reusable) data principles in dentistry, allowing trustworthy and transparent AI applications.