Wednesday, October 01, 2014

Intelligence cannot be commoditized

In the first posting, we saw how the enterprise world needs to be realistic regarding its expectations of data science tools. In this second posting, we will be looking at why it is still essential to embrace them sooner rather than later or else they run the risk of suffering dire consequences.

Intelligence cannot be commoditized

Companies must understand that they need to rapidly embrace data analytic methods as they are essentially the next stage of evolution in the enterprise toolkit for understanding and leveraging information to gain competitive advantages. While enterprises should not just jump and sprinkle “bigdata” everywhere, but they also should not wait too long to embrace these technologies as you cannot commoditize Intelligence.

Almost everything else is commoditizing over time, albeit at different pace but nonetheless it is evolving from custom solution to product and finally to utility. However, for data science tools, the opposite actually happens. Data and knowledge improve by use and accumulation of information and one cannot hope that it will be commoditized for the particular enterprise at some point.

In short, “thee” who has the best data (and uses it) wins. If you wait too much to collect and learn from it you are giving an advantage to your competitors that you may never recover from. By adopting such techniques early on, an enterprise will gain an edge over the rest of the actors within its ecosystem as they will now have to catch up. Moreover, very quickly, big-data - machine learning tools - analytic models will become widely available, as well as affordable - and these same tools will rapidly devolve to become another weapon in the market Red Queen’s race.

As you can see it can be rather dangerous to delay the adoptions of these novel methods within a given enterprise because of its intrinsic nature. In the following posting, I will look into the double edge sword that is the data itself.

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