Harnessing the Heart of Big Data

The exponential increase in Big Data generation combined with limited capitalization on the wealth of information embedded within Big Data has prompted us to revisit our scientific discovery paradigms. A successful transition into this digital era of medicine holds great promise for advancing fundamental knowledge in biology, innovating human health, and driving personalized medicine; however, this will require a drastic shift of research culture in how we conceptualize science and use data. An e-transformation will require global adoption and synergism among computational science, biomedical research, and clinical domains.A scarce number of scientific investigations have innovated clinical diagnosis, prognosis, and therapeutics, despite decades of research and enormities of National Institutes of Health (NIH)–funded research dollars.1,2 This situation requires a global reassessment of whether linear thought processes and reductionistic approaches alone can describe biological processes in a way that translates to valuable information on human systems. Information gleaned from population science using large Big Data data sets has perpetuated a shift in the paradigm of how we define and investigate health and disease in the individual patient.3 We are recognizing the profound value in unorthodox data types and in the integration of diverse data to describe individuals to sufficient depths for discerning clinical outcomes. Biomedicine, along with other fields, has been awakened and awed by the digital wave of major corporations such as Google and Amazon, who have revolutionized the Internet roadmap through developing and refining sophisticated data analytics platforms to accurately describe individual human behavior.4 The reality in biomedical science is that there are zettabytes of high-quality data sitting idly on servers and in cloud infrastructures, and an abundance of biomedical knowledge lies hidden within, yet only a small fraction of this wealth has been harvested. There is an immediate need for data science to …