Please do not adjust margins Process data contains diverse information, such as solvents and the order of additives, which are primarily qualitative variables, thus requiring careful consideration for the mathematical representation. However, such representation comparison of process data has never been tackled, making the effectiveness and limitation of process informatics unclear. In this work, we analyzed the effectiveness and limitations of PCE prediction considering all material and process variables within the Perovskite Database. We compared several treatments of process data with delimiters, identifying a suitable data representation in machine learning. The interpretation of the machine learning model allowed us to find the relative influence of materials and processes on the PCE. We also analyzed the origin of regression error, clarifying the limits of machine learning due to data degeneracy. This work should contribute to the data-driven development of perovskite solar cells.