 Much recent philosophy of science, and particularly modelling and experimentation, has challenged theory-centrism by highlighting the role of models, methods and modes of intervention as research outputs rather than simple tools, and stressing the importance of expanding philosophical understandings of scientific knowledge to include these elements alongside propositional claims. The rise of big data offers another opportunity to reframe understandings of scientific knowledge as not necessarily centred on theories and to include non-propositional components—thus, in Cartwright’s paraphrase of Gilbert Ryle’s famous distinction, refocusing on knowing-how over knowing-that (Cartwright 2019). One way to construe data-centric methods is indeed to embrace a conception of knowledge as ability, such as promoted by early pragmatists like John Dewey and more recently reprised by Chang, who specifically highlighted it as the broader category within which the understanding of knowledge-as-information needs to be placed (Chang 2017).