In a typical metabolomics experiment performed with liquid chromatography/mass spectrometry, it is common to detect thousands of signals with unique m/z values from a biological sample. Understanding these data is difficult and often recognized as the rate-limiting step of metabolomics. Although the challenge may seem to be primarily an informatic problem, experimental variables also significantly complicate data interpretation. In this talk, a critical perspective of factors contributing to the complexity of metabolomic data will be presented. Opportunities using intelligent MS acquisition and benchmarking strategies to reduce the analysis burden will be outlined. Guidelines for accurately assessing metabolism from metabolomic datasets and complementary biochemical assays will be discussed.