Big data: Volume, Variety, Velocity, Veracity - CEBM

Last week, a student asked me whether our new MSc module "Big Data Epidemiology" would be covering "machine learning" techniques and enthusiastically told me all about how they intend to apply such techniques to their own research. The short answer to the student's question was "Yes, but only briefly". The long answer requires some exploration into what we mean by "big data epidemiology" and consideration of what machine learning can (and perhaps more importantly, cannot) do for researchers. Data is often considered "big data" if it can be described in terms of the "four V's": volume (there's a lot of it), variety (the data takes lots of different forms), velocity (the data changes or is updated frequently) and veracity (the data may be of poor/ unknown quality).1 On our module we use electronic healthcare records databases that can contain information about millions of patients, collected over several years (volume).