Big Data Reduction 3: From Descriptive to Prescriptive

There are some well tested industry definitions for the "analytics value chain" from "descriptive" to "predictived" to "prescriptive" which make more sense, I believe. Note also that the term "Prescriptive Analtyics" is a (tm) registered by someone already doing years of deep research on this - and already delivering technological solutions that work.  Probably some historical context would be good.

For me, the factor missing here is "time" as in "real-time".   As Rafiki in The Lion King said: "What does it matter, it is in the past". As we proliferate Big Data, the analytics of greatest interest, are on the most recent data. Relevence is a function of "time," and especially so for Social Analytics.

In the end, as data from social interactions ages (gets older), it's no longer relevent.  Hence, the concept of "prescriptive analytics" for "social" are going to be processed in-real time on real-time streams of data.  These are still HUGE in size and scope, but usurp anyt meaningful interest in large volumes of historical data.  I suggest we lessen our use of "extracted from" as that implies collecting, storing, managing data..., which is of less interest than knowing what is going on "right now."

The big analytics companies are wrestling with this now. As the business community changes instantly, pivot strategies are no longer something that can be done in October's annual budget process. Hence prescriptive analytics for strategy are rapidly shifting to sensitivity analysis on real-time streams of data from social content. Businesses that rely on the last five years of historical data to make strategy decisions for the immediate or mid-term history are likely to follow the US auto industry circa 2007/8.

Industries that will really benefit from this include Healthcare (I want the best medical algorithmic solutions in my diagnosis, than what was done last year with my current, and dynamically changing symptoms that can be processed in real-time with advanced sensor technology.

Another will be Resources and Energy - from Upstream to Downstream , to Retail. Think "distribution, "the grid," and "smart meters."  The potential for efficiency here is immense.

Another will be government and defense. One of the last bastions of innovation adoption is in government. It will take this kind of shift - for government and legislation to know what the public is thinking, and deliver on represntation in a way we can only imagine.  Defense is already going this way.

Last point to drive the real-time concept here is that everything, and I mean EVERYTHING will have a sensor on it. or in it. From your pet, to your car to your kid's lunch box.  Let your mind wander as you ponder this one.  Check out #IndustrialInternet from GE - this is BIG.