 New institutions, communication platforms and regulatory frameworks also emerged to assemble, prepare and maintain data for such uses (Kitchin 2014), such as various forms of digital data infrastructures, organisations aiming to coordinate and improve the global data landscape (e.g., the Research Data Alliance), and novel measures for data protection, like the General Data Protection Regulation launched in 2017 by the European Union. Together, these techniques and institutions afford the opportunity to assemble and interpret data at a much broader scale, while also promising to deliver finer levels of granularity in data  analysis.[1]  They increase the scope of any investigation by making it possible for researchers to link their own findings to those of countless others across the world, both within and beyond the academic sphere. By enhancing the mobility of data, they facilitate their repurposing for a variety of goals that may have been unforeseeable when the data were originally generated. And by transforming the role of data within research, they heighten their status as valuable research outputs in and of themselves. These technological and methodological developments have significant implications for philosophical conceptualisations of data, inferential processes and scientific knowledge, as well as for how research is conducted, organised, governed and assessed. It is to these philosophical concerns that I now turn.