Molecular genetic studies of lymphoma have led to refinements in disease classification in the most recent World Health Organization update. Nevertheless, a ‘one-size-fits-most’ treatment strategy based on morphology remains widely used for lymphoma despite significant molecular heterogeneity within histopathologically-defined subtypes. Precision medicine aims to improve patient outcomes by leveraging disease- and patient-specific information to optimise treatment strategies, but implementation of precision medicine strategies is challenged by the biological diversity and rarity of lymphomas. In this review, we explore existing and emerging real-world data sources that can be used to facilitate the development of precision medicine strategies in lymphoma. We provide illustrative examples of the use of real-world analyses to refine treatment strategies, provide comparators for clinical trials, improve risk-stratification to identify patients with unmet clinical needs and describe long-term and rare toxicities.