Mathematical modeling of chemical reaction kinetics has been proven to aid the development of new reactions and processes. Chemical kinetic modeling is a well-established principle in chemical engineering that uses fundamental knowledge of the reaction mechanism to predict conversion data. In pharmaceutical drug development, elementary-type kinetics are hardly common, because of the nature of the complex organic reaction mixtures and low-level impurities. Thus, data-driven modeling plays an important role in understanding the relationship between reaction parameters and reaction profiles. Advances in reaction automation technologies, such as high-throughput platforms and autosamplers, enable greater data collection to enrich our understanding of chemical reactions. As a result, statistical analysis has shifted from conventional end-point analysis to modeling the entire reaction profile using more advanced statistical models. Data-driven approaches are especially useful in early stage of development where not enough time or material is available for a proper kinetic model development. For the same modeling task, regardless of the underlying approach, we strongly feel that a systematic process of model development needs to be applied. We developed a rigorous and general modeling workflow describing how to apply kinetic models and statistical models to a set of dynamic reaction data. In particular, a semiparametric model was applied. An industrial case study is presented with a methyl ester chemoselective hydrolysis reaction, to showcase the performance and robustness of the two modeling approaches and their impacts, side by side, on parameter effect estimation, reaction robustness range finding, and reaction optimization and operation window prediction. New and innovative visualization techniques are shared in this article for efficient data and model result interpretation.