 We are witnessing a progressive “datafication” of social life. Human activities and interactions with the environment are being monitored and recorded with increasing effectiveness, generating an enormous digital footprint. The resulting “big data” are a treasure trove for research, with ever more sophisticated computational tools being developed to extract knowledge from such data. One example is the use of various different types of data acquired from cancer patients, including genomic sequences, physiological measurements and individual responses to treatment, to improve diagnosis and treatment. Another example is the integration of data on traffic flow, environmental and geographical conditions, and human behaviour to produce safety measures for driverless vehicles, so that when confronted with unforeseen events (such as a child suddenly darting into the street on a very cold day), the data can be promptly analysed to identify and generate an appropriate response (the car swerving enough to avoid the child while also minimising the risk of skidding on ice and damaging to other vehicles). Yet another instance is the understanding of the nutritional status and needs of a particular population that can be extracted from combining data on food consumption generated by commercial services (e.g., supermarkets, social media and restaurants) with data coming from public health and social services, such as blood test results and hospital intakes linked to malnutrition. In each of these cases, the availability of data and related analytic tools is creating novel opportunities for research and for the development of new forms of inquiry, which are widely perceived as having a transformative effect on science as a whole.