Artificial intelligence (AI) is fundamentally dependent on the data used for its training and operation. This chapter addresses the data-centric challenges that underline AI applications in the public health domain. It begins with a map of key data concepts and categorizes relevant data types to contextualize their role across various stages of AI development. The chapter discusses the specific data issues, such as availability, quality, usefulness, bias, and privacy and security. By systematically analyzing these challenges, this chapter provides public health students, researchers, data scientists, and policymakers with the essential knowledge to anticipate, identify, and mitigate data-related risks. Those concepts are a key component of data sciences and AI literacy for AI in public health.