The life sciences permeate so many aspects of our individual lives—from our environment to our health care, and to a discourse about stem cell research and genetic testing. While spectacular scientific advancement has been witnessed in recent decades, so much remains unknown in the field of life sciences. Sensors and other wearable devices that interface with digital apps such as glucose monitors can be worn by individuals who have connectivity with providers of healthcare, however, it is a herculean task to convince and train individuals with little or no knowledge of data science to accept Big Data whether for decision-making on the part of the healthcare providers or privacy assurance on the part of patients. Moreover, aggregating the ever-increasing information surrounding the multiple scales for what constitutes a disease remains a research problem. Therefore, there is a need to model these scales through Big Data integration, whereby the model evolvement and development will facilitate feasible prediction for individuals. In this paper, Big Data is proposed as an extremely huge dataset on which a model can be trained for prediction tasks relating to human behavior and interactions in the field of life sciences. This paper aims to synchronize life science with data science using Big Data analytics to improve healthcare services and aggregate increasing information surrounding the multiple scales for what constitutes a disease.