Good question. I am not an expert but here’s my take from my time in this space. Big data folks typically do sampling and such, but that doesn’t eliminate the

Good question. I am not an expert but here’s my take from my time in this space. Big data folks typically do sampling and such, but that doesn’t eliminate the need for a big data environment where such sampling can occur. Just as a compiler can’t predict every branch that could happen at compile time (sorry VLIW!) and thus CPUs need dynamic branch predictors, so too a sampling function can’t be predicted in advance of an actual dataset. In a large dataset there are many ways the sample may not represent the whole. The real world is complex. You sample away that complexity at your peril. You will often find you want to go back to the original raw dataset. Second, in a large organization, sampling alone presumes you are only focused on org-level outcomes. But in a large org there may be individuals who care about the non-aggregated data relevant to their small domain. There can be thousands of such individuals. You do sample the whole but you also have to equip people at each level of abstraction to do the same. The cardinality of your data will in some way reflect the cardinality of your organization and you can’t just sample that away.