The hope, hype and reality of Big Data for pharmacovigilance

journals.sagepub.com/home/taw 5 The concept of ‘Big Data’ is not new, having been in frequent use since the 1990s. In recent years, the term has become more relevant, with increases in the size and type of data available, as well as the computational capability to rapidly execute analyses across large datasets. ‘Big Data’ is typically characterized by at least ‘3 Vs’: velocity, variety and volume; some posit that ‘veracity’, ‘variability’ or ‘value’ should also be added to the list.1 For many years, Big Data has been described as having the potential to result in transformative changes to many industries, including healthcare. While there have been significant advances in its use in the financial and retail sectors, its impact in healthcare has been slow, in part due to regulations and privacy concerns. In drug and vaccine safety, in particular, its postulated benefits have not fully materialized. In this editorial, we examine the impact of Big Data on the cornerstones of post-approval pharmacovigilance: quantitative signal evaluation and the identification of potentially new safety signals. We describe the achievements thus far of Big Data approaches within pharmacoepidemiology; the capabilities and approaches that are most promising for improving the quality of data available for drug safety research; and, lastly, the importance of evaluating whether the contribution of Big Data sources to identifying potential safety signals is redundant, complements or replaces the traditional data sources and techniques being used in pharmacovigilance today.