Manual data processing is a tedious task that should be automated. Besides saving time, automated data processing also fights other problems in chemistry. Automated data processing in a normalized way makes data analysis between different experiments possible and can remove biases. In this study, kinetic data workflow is studied and a python script for automated data processing is made. Different experiments on the hydrogenation reaction of ethyl hexanoate using a ruthenium-PNN catalyst have been performed to obtain kinetic hydrogenation data. Reaction temperature was set to either 50, 70 or 110 °C and catalyst loading was either 50, 100 or 200 ppm. In total, nine experiments were performed. Gas chromatography and pressure readings during the reaction are used for analysis. Analysis is done on two different machine that report data in a different template and format. Therefore, a python script was written to automatically process the raw data obtained by these analysis methods. The written script imports obtained data, calculates new normalized parameters, concentration of different species for example, creates different plots of processed data and exports an excel file containing normalized data. This exported excel file was used to further examine catalyst kinetics. It was found for example, that at 50 and 70 °C, the reaction order of the catalyst is below one and at 90 and 110 °C catalyst reaction order is above one. The made processing script has improved data workflow, while data processing time has been reduced.