Last year (September 2013 – https://iiom.wordpress.com/2013/09/15/c-levels-deserve-game-changing-intelligent-analytics/) I wrote about the use and emergence of ‘Intelligent Analytics’.  As with so many things in the lead edge/innovation arena it takes time for the market to catch up with the notion of newness.  While we wait for the flood gates to open while a trickle of promise grows day-by-day there comes along yet another new form of analytics…. preceptor analytics.   Most use analytics as a means of predicting and guiding future behavior.  Unfortunately this is only as good as the information you have and the intelligence you can apply to it.  As is often the case the use of intelligence is extremely hindered by past prejudice, procrastination, timidness and social pressures that treat anything ‘foreign’ as being something to stay clear of.  In the latter case we are more apt to adopt a wait and see, or let someone else make the first move type of position only to regret later the opportunity that we missed as a result.

The next stage in the analytics area is ‘preceptor analytics’, but what is it?  Preceptor analytics rather than try and predict based on current behavior takes a bit of a different view by focusing its attention on the root cause(s) of the data/information results that we have at hand.  By way of a simple example, rather than treat the virus we focus our attention on the causes of the virus.  As we see signs that the data/information is behaving in a certain fashion we aggressive focus our intention on the various causes as a means to drive possible predictions.  But because we also wish to have at least one additional point of confirmation we would also be using the predictive analysis that has been used traditionally as well.  When we compare preceptor behavior against predictive results we are then able to get a sense of potential predictability.  In effect we are narrowing guessing via an acquired understanding of the causes.

The concept, while very new in the data analytics segment, is not new in other sciences in which prediction is a big part of the task.  Such areas as meteorological, seismic, and astrological are a few of the sciences that blend pre- and event based analytics under one summation model.

Does the end justify the means?  I feel that it does, not because our predictive analytics are bad but because we need to be able to challenge those very same analytics based on root cause.  So with that I will leave it up to the pungents to challenge the notion but I will put more reliance on hearing what real users of analytics have to say (and whether they have considered or use such a means of confirmation).

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