We have demonstrated how a clean-data-centric approach combining standardized experimental procedures, designed to capture the kinetics of active catalyst states from catalyst precursors, with interpretable AI enables the identification of key physicochemical parameters correlated with catalyst performance in selective oxidation. These correlations were determined for the oxidation of alkanes with different chain lengths under different reaction conditions. While the correlations reflect to some extent the previous empirical knowledge on the reactions and materials, and thus provide a proof of principle of the method, they had not yet been identified in their full complexity in conventional studies. Large-scale energy storage and efficient material economic use of resources require the mastering of complex catalyzed reactions at interfaces. The integration of rigorously conducted experiments according to standard operating procedures and interpretable data analysis presented here points the direction to breaking the inherent barrier in developing better catalysts for exactly such reactions.