These scientific and technological advances are coupled also with a need for greater discussion between members of the catalytic community, and advocacy of standardisation. Whilst the principles of findable, accessible, interoperable, and reusable (FAIR) data have developed strong roots in the computational modelling domain, the distribution or centralisation of experimental data remains limited, and focused on positive results. The value of all data should be championed, and the importance of metadata to aid users in understanding value and limitations of a given dataset; deposition of results in an accessible resource should be encouraged, especially for experiment, where uptake is more urgently needed. The communication between researchers should include experimental and computational communities, and span academia, industry, and third-party organisations, at all levels of scientific investigation, in order to deliver better understanding of data needs and standardisation of data-collection procedures. The work here is implicitly multidiscipline, and so the interaction of chemists, materials scientists, physicists, computer scientists, data scientists and other domain experts should be encouraged to maximise the opportunity for multi-fidelity models that address shortcomings arising in individual research domains. Finally, there is the need to train and distribute knowledge among researchers of the value of their data; we should be educating in a cross-disciplinary manner about the importance of detailed digital data collection, in both experiment and computation. Such action will lead to engagement and investment towards necessary tools to accelerate the big-data driven discovery in heterogeneous catalysis; such software capabilities already exist, driven by the explosion in interest towards data-driven discovery, but the potential is yet to be realised.