MS offers a flexible and precise alternative to traditional methods for allergen detection and quantitation. However, this flexibility also engenders many ways of acquiring information and translating it to simple, clear data useful to end-users. Currently, methods for performing data analysis for allergen detection by MS are unstandardized, and it is therefore difficult to compare different analytical methods. We identify three key components of data analysis: detection of positive signals, calibration, and signal integration. For each of these components, there are multiple pathways available for method developers. We discuss these alternative methods, giving examples from literature. Assuming that the end result of an allergen analysis should be clear, unambiguous, and understandable to all relevant stakeholders, we pay particular attention to the consequences of each choice to the analysis in question and, where appropriate, suggest best practices. We also identify data analysis criteria that should be clearly delineated in the reporting of a method. Establishment of community-wide standards for unambiguous reporting of data analysis workflows will improve the evaluation, comparability, and transferability of methods.