 Philosophers have long acknowledged that data do not speak for themselves and different types of data require different tools for analysis and preparation to be interpreted (Bogen 2009 [2013]). According to the representative view, there are correct and incorrect ways of interpreting data, which those responsible for data analysis need to uncover. But what is a “correct” interpretation in the realm of big data, where data are consistently treated as mobile entities that can, at least in principle, be reused in countless different ways and towards different objectives? Perhaps more than at any other time in the history of science, the current mobilisation and re-use of big data highlights the degree to which data interpretation—and with it, whatever data is taken to represent—may differ depending on the conceptual, material and social conditions of inquiry. The analysis of how big data travels across contexts shows that the expectations and abilities of those involved determine not only the way data are interpreted, but also what is regarded as “data” in the first place (Leonelli &amp; Tempini forthcoming). The representative view of data as objects with fixed and contextually independent meaning is at odds with these observations.