Alongside experiment, the advancement of computational capabilities allows the "bottom-up" interrogation of elemental and structural knowledge from across the periodic table, presenting significant opportunities for accelerated data-driven discovery. Promising materials can be considered further using parameterised models to explore surface structures and composition as a function of operating conditions, and reaction mechanisms derived using automated construction of chemical reaction networks, providing vast quantities of data relating to a reaction landscape from which rates and product distributions are accessible via kinetic modelling. With the knowledge calculated within this sampling space, the efficacy of the catalytic system can be linked against key "descriptors" of the catalyst and its operating conditions, providing powerful shortcuts when navigating across the reaction landscape to find better catalysts via e.g., active learning protocols. In the most state-of-the-art approaches, descriptors are derived as compound functions of both experimental and computational information, via multi-fidelity data models. Current and Future Challenges Data-driven models are dependent on large, accurate, and complete datasets, yet such experimental data remains challenging to locate, access, use, or reproduce. Historically, the reward structure of the research community has been towards positive results, which means that negative results are not shared. Incomplete datasets lead to sampling bias and inaccuracy of data models; furthermore, hidden data can also present a challenge for reproducibility, whereby not all the experiment parameters are reported for future investigators. Data quantity and quality are also important aspects, yet most experimental investigations typical focus in a small chemical space, which lead to small datasets. Indeed, data completeness can again become challenging when only the "best" catalysts are considered for higherlevel characterisation methods, such as in situ electron microscopy; and simultaneously, data quality is compromised, as differing standards of analysis are introduced, and outcomes reported in contrasting formats. The combination of identifiable data sources is also a current challenge, as the quality and quantity of information can vary in relation to synthetic methods, catalytic testing, and characterisation; and these data may be embedded in images, making collecting accurate data a challenge.