Insight and experience, concentrated

Does Big Data Always Equal Big Understanding?

ipv4data

 

** This is a re-post of a past article from 2013 that is highly relevant today.  Some lessons need constant reminders to take hold.

 

Lots of chatter about Big Data out there these days. Huge possibilities lie ahead for those who can use it smartly, but there’s a flip side for those who jump aboard without a good understanding of what it really represents. Some of the cautionary discourse centers around privacy fears, potential for misinterpretation, or even stunted creativity in favor of easy commercial successes.

Big Data may not lie, but it doesn’t always tell the whole truth either. Making the leap to assume that chasing raw data on grandiose scales equals understanding is similar to assuming that more complexity in computing operations equals sentience. No matter how complete your quantitative dataset, it still needs scrutiny and interpretation to be meaningful.

An intriguing approach is to maintain the complexity and depth of the original data – but dramatically improve the accessibility.  Rather than present your pre-packaged analysis of the inputs, empower your audience to interrogate the inputs directly and construct their own conclusions.  Perhaps not the ideal approach for a short presentation by a consultant, but maybe perfect to build an engaged audience?  I’m interested to see where this can lead and how those bounds can be blurred…

 

“The real danger is not that computers will begin to think like men, but that men will begin to think like computers”
– Sydney J. Harris