The term ‘big data’ has become common parlance in the last several years as high throughput technologies have ramped up during the early years of this millennium. However, not surprisingly, the term has come to mean different things to different people. The often siloed proteomics community has naturally taken it to include, even center on, the output of millions of mass spectra and their interpretations that can and are being generated regularly (and are finally being more efficiently archived). This inward looking view is unfortunately neither correct nor widely shared with the rest of biomedical research community, particularly with those with interests in translational medicine. The all important field of cancer research stands out in this regard. Indeed, genomics, in all its ramifications, has not only co-opted big data (as a concept) but also has garnered a lot of the associated resources allocated for it and metabolomics, a Johnny-come-lately relatively, is a fast rising star as well. It can be argued that neither of these two ‘omic disciplines has the potential value that proteomics offers because of its inherent complexity (and hence information content) but its singular lack of success so far in providing diagnostic/therapeutic findings largely obscures and nullifies this. Genomics and to a lesser degree metabolomics may well be examples of ‘picking the low hanging fruit’ because the data they produce and the analyses thereof are far simpler than those of most large scale proteomic experiments, being largely excluded from this big data club will be costly in the basically zero sum game of research funding allocations. The proteomic community needs to reflect on this situation and discuss how to get on board before this train leaves the station.