There are multiple technological advances identifiable to meet the challenges and fully achieve the potential of data-driven approaches. Within the laboratory, greater accessibility of automated highthroughput facilities, capable of synthesising, testing, and characterising catalysts, will be powerful in facilitating on-the-fly data-driven catalyst discovery, and must be coupled with public accessibility in centralised repositories to achieve larger, consistent, and more complete datasets. For modelling, improved software models are still needed to simulate a more accurate description of complete catalytic conditions, including the effects of temperature, pressure, and solvents, to provide accurate surrogate models of energy landscapes that can be explored rapidly, with automated discovery again an opportunity. And at the interface of computation and experiment, greater integration of catalytic datasets to provide holistic coverage is necessary to account for deficits in knowledge from either the experimental or computational domains alone; indeed, one needs to harness the individual strengths of "top-down" and "bottom-up" perspectives to derive complementary data, rather than distinct.