The concept of "big data" research-the aggregation and analysis of biologic, clinical, administrative, and other data sources to drive new advances in biomedical knowledge-has been embraced by the cancer research enterprise. Although much of the conversation has concentrated on the amalgamation of basic biologic data (e.g., genomics, metabolomics, tumor tissue), new opportunities to extend potential contributions of big data to clinical practice and policy abound. This article examines these opportunities through discussion of three major data sources: aggregated clinical trial data, administrative data (including insurance claims data), and data from electronic health records. We will discuss the benefits of data use to answer key oncology practice and policy research questions, along with limitations inherent in these complex data sources. Finally, the article will discuss overarching themes across data types and offer next steps for the research, practice, and policy communities. The use of multiple sources of big data has the promise of improving knowledge and providing more accurate data for clinicians and policy decision makers. In the future, optimization of machine learning may allow for current limitations of big data analyses to be attenuated, thereby resulting in improved patient care and outcomes.