Different from, but also complementing the existing literature on metabolomic data analysis, this study looks at the influence of data pre-processing (e.g., collection parameters, such as intensity threshold and mass tolerance) and pre-treatment methods (e.g., scaling and data transformation) on the statistical models generated and feature selection, using an LC-MS-based untargeted metabolomic dataset. This was in order to actually demonstrate to what extent steps in the data analysis pipeline impact on the output, which would certainly affect the downstream biological interpretation. Thus, the study clearly points out, with illustrative examples, that the methods employed in the data analysis should not be regarded as “one-size-fits-all”. As such, this study intends to make a contribution to the on-going discussions in the metabolomics community with regard to ways of maximizing the value of untargeted metabolomics data and influence/effects of data analysis steps on the downstream analyses and interpretation . Ultimately this work emphasizes the importance of understanding the structures of raw data, and exploration of various algorithms and parameters are vital and mandatory in data mining to maximize the value of generated data.