We are at the dawn of a data deluge in health that carries extraordinary promise for improving the health of populations. However, current associated efforts, which generally center on the 'precision medicine' agenda, may well fall short in terms of its overall impact. The main challenges, it is argued, are less technical than the following: (1) identifying the data that matter most; (2) ensuring that we make better use of existing data; and (3) extending our efforts from the individual to the population by exploiting new, complex, and sometimes unstructured, data sources. Advances in Epidemiology have shown that policies, features of institutions, characteristics of communities, living and environmental conditions, and social relationships all contribute, together with individual behaviors and factors such as poverty and race, to the production of health. Examples are discussed, leading to recommendations that focus on core priorities for data linkage, including those relating to marginalized populations, better data on socioeconomic status, micro- and macro-environments, collaborating with researchers in the fields of education, environment, and social sciences to ensure the validity and accuracy of multilevel data, aligning research aims with policy decisions that must be made, and heightening efforts to protect privacy.