Luc Burgelman – December 9, 2014
Recently, I talked about defining big data, and how there seems to be some confusion in the market around what, exactly, constitutes this term that we’re seeing everywhere. But big data is just the tip of the iceberg. Big data analytics have taken on all sorts of forms and functions, as well. Whether it’s charts and graphs that show images of the business or prescriptive analytics that provide recommendations in real time, there’s an abundance of definitions for big data analytics, too.
One of the articles I linked to in my post about defining big data was an interview with Ventana Research’s Tony Cosentino (@TonyCosentinoVR). In it, he talks about the “Three V’s” of big data. He says that, “…if we think about big data analytics in terms of the Vs (volume, velocity and variety), the research shows that organizations prioritize these by variety, volume and then velocity.” He goes on to say that thinking of analytics this way tends to turn big data into an ethos instead of something tangible that delivers value to an organization.
I couldn’t agree more.
So when Mr. Cosentino talks about prioritizing the Three V’s of velocity, volume and variety, I actually think that’s not the full picture. The only way for analytics to deliver real, tangible value is to combine all three of them. You’ve got to take large data sets (volume) from disparate sources (variety) coming at you at the speed of light (velocity) and provide recommendations in real time. Removing any of them from the equation is like chopping off the leg of a barstool – it leaves you without support.
The industry is moving in the right direction – but until the default definition of big data analytics centers around the value it brings to the organization, we’ve still got some work to do.