Using the same raw dataset, and exploring and applying different methods and algorithms in handling the data, the study clearly demonstrates how crucial the data pre-processing and pre-treatment steps are in a metabolomics data analysis pipeline. These steps significantly affect the chemometric models computed downstream, including the variation explained by the models, the classification accuracy and the quality of the models. However, the inferred observations from this study are limited, as being drawn from a single dataset (from plant samples). Applying the same exercise to different datasets from other sample types (e.g., cell extracts, biofluids, etc.) might provide more insights and subsequently a formulation of generalized guidelines. Thus, it suffices here to point out that an understanding of the data structures and the approach adopted for handling a specific untargeted metabolomic dataset will definitely influence the data analysis outcome, as demonstrated in this study. Stepping forward from untargeted metabolomic data, to information, and finally knowledge, is not a trivial endeavor or a simple “syllogistic” approach.