In information technology, big data consists of data sets that grow so large that they become awkward to work with using whatever database management tools are on-hand. For that matter, how big is big? It depends on when you need to reconsider data management options – in some cases it may be 100Gb, in others, it may be 100Tb. So, following up on our earlier post about big data and insight, there is one more important consideration:
Does insight equal decision?
The foregone conclusion from big data proponents is that each nugget of “insight” uncovered by data mining will somehow be implicitly actionable and the end user (or management) will gush with excitement and praise.
The first problem is how can you assume that “insight” is actionable? It very well may not be, so what do you do then? The next problem is how can you convince the decision maker that the evidence constitutes an imperative to act? Absent action, the “insight” remains simply a nugget of information.
Note that management typically responds to “insight” with skepticism, seeing the message bearer as yet another purveyor of information (“insight”) and insisting that this new method is the silver bullet, thereby adding to workload.
Being in management myself, my team often comes to me with their little nuggets … some are gold, but some are chicken. Rather than purvey insight, think about a recommendation backed up by evidence.