This article seems to be discussing a very different 'Big Data' to that I see. I'm sure the author's points are true in their world, but largely they aren't in

This article seems to be discussing a very different 'Big Data' to that I see. I'm sure the author's points are true in their world, but largely they aren't in mine (mostly online media stuff). Here's a rebuttal of a sort: This data collection is a recent phenomenon: only since media moved online has it been cheap enough to collect sufficient data to do interesting things. Furthermore, you can actually use that data is more meaningful ways (e.g. recommendation systems) that weren't possible before. That value is not being extracted by the companies that do currently collect data: surprisingly true from my discussions with media companies. The largest companies (Yahoo!, Netflix, etc.) sure do, but the second tier don't. That companies will need outside help to extract insights: This perhaps indicative of the biggest difference between our worlds. It seems the author is talking about batch jobs, where you do some rudimentary analysis on a data, makes some slides, present to management, and go home. The people I know are talking about deploying automated systems into production (think recommendation systems again). This requires a large array of skills (machine learning, software engineering, distributed systems, etc.) that many companies don't have. "Minitab, SAS, SPSS, Systat" are not sufficient tools in this world. The insights gathered from ever larger data sets have more value and are more accurate than insights gathered from smaller data sets: The conventional wisdom in machine learning is that more data trumps more sophisticated algorithms. E.g. http://anand.typepad.com/datawocky/2008/03/more-data-usual.html http://anand.typepad.com/datawocky/2008/03/more-data-usual.h... Unstructured and cross functional data have huge value waiting to be extracted: Here we agree!