Big Data: Are the 3V’s the Sole Problem?
Big data has clearly captured our attention.
As of this writing there are 649,717 job titles that contain the words Big Data on LinkedIn, 39,773 job openings for people that contain big data as a required job skill, 1,878 Groups that contain Big Data as a part of their reason for being and 12,548 companies that include big data as a part of the offerings in one form or another. Big Data has become so top of mind, there are now dozens of conferences addressing the topic. One can even earn a Big Data degree from one of many prestigious Universities around the globe.
As I listen to specialists and influencers articulate their thoughts around big data, I find myself periodically drifting back to the same questions: Why does the big data conversation often stop at Volume, Variety and Velocity (AKA 3V)? Are these the sole problem? Or is it also understanding the corporate knowledge trapped inside the data? Is it about the machines helping people understanding what the data represents and assisting people in finding the most relevant pieces in a short moment’s notice?
It has been agreed that the technical challenges of managing the ever increasing and exceedingly large volumes of content can be met. It seems to me that the focus should equally cover accurately identifying, capturing and understanding the business value contained in the data. In my mind, understanding the context hidden in the content as the true challenge and I found it curious that it was not talked about with the same intensity and determination as the 3V’s.