With the rapid growth of health-related data including genomic, proteomic, imaging and clinical, the arduous task of data integration can be overwhelmed by the complexity of the environment including data size and diversity. This report examines the role of data integration strategies for big data predictive analytics in precision medicine research. Infrastructure-as-code methodologies will be discussed as a means of integrating and managing data. This includes a discussion on how and when these strategies can be used to lower barriers and address issues of consistency and interoperability within medical research environments. The goal is to support translational research and enable healthcare organizations to integrate and utilize infrastructure to accelerate the adoption of precision medicine.
Keywords: common data models; data integration; infrastructure; infrastructure-as-code; interoperability; multiomics; precision medicine; predictive analytics; sociotechnical; virtual machines.