Big Data has a Big Role in Biostatistics with Big Challenges and Big Expectations

Big data has now gone beyond a conceptual construct to a viable working approach for addressing and making use of huge volumes of data. The expectations of big data applications and outcomes in healthcare such as: a 20% decrease in patient mortality, better information regarding patient health and symptoms, reducing readmission, better point of care decision making, integration of smart devices and sensors with data bases, everyday genome sequencing, developing a treatment approach for cancer, and assessing the risk of readmission [1-3]. There are six key characteristics of big data that impact its use in healthcare: volume, velocity, variety, veracity, validity, and volatility. These impact the use of big data methods. There are several types of challenges in the use of big data in biostatistics: statistical verifiability, computational load, and technical learning curve.