On Data, Big Data and Social Research. Is It a Real Revolution?

This chapter aims at discussing critically some epistemological assumptions underlying a data science for social research. For this purpose, it is discussed the general notion of big data and the meaning of key-concepts such as those of information and data, mainly considering contributions coming from the science and technology studies (STS) and the sociology of quantification. In particular, it is argued the necessary shift from a discrete and transportable definition of data to a processual one, also taking into account the fact that data are always a process both when they are produced and when they are used/analysed in order to have research’s results. The notion of data-base is compared with that of infrastructure as defined in STS, so that it is clear that they cannot be considered as repositories from which it is possible to extract meanings or results like getting minerals from a mine. Data and data-base are processes which cannot begin without a research question. For these reasons the debate opposing hypothesis-driven versus data-driven research should be overtaken: in social research, as well as in hard sciences, data-driven research simply doesn’t exist. The last paragraph is devoted to draw some conclusions from the previous discussion in the form of hopefully useful suggestions for developing a data science for social research.