by Angela Guess
Paul Miller recently wrote an article for Sys-Con Media discussing the idea of Data Gravity, Dave McCrory’s concept that “large or valuable agglomerations of data exert a pull that tends to see them grow in size or value.” Miller writes, “I caught up with Dave over skype last week, just before he launched DataGravity.org and proposed a formula for getting some numbers into the discussion. As a concept, Data Gravity seems pretty closely associated with current enthusiasm for Big Data. And, like Big Data, the term’s real-world connotations can be unhelpful almost as often as they are helpful. Big Data is generally accepted to exhibit at least three characteristics, which are Volume, Velocity and Variety. Various other V’s, including Value, also get mentioned from time to time, but with less consistency.”
He continues, “And yet, Big Data’s name says it’s all about size. Size (volume) matters. The speed with which data must be ingested, processed or excreted is less important. The complexity and diversity of the data doesn’t matter either. And that’s nonsense, of course. On its own, the size of a data set is neither here nor there. Coping with lots of data certainly raises some not-insignificant technical challenges, but the community is actually doing a pretty good job of coming up with technically impressive solutions. The interesting aspect of a huge data set isn’t its size, but the very different modes of working that become possible when you begin to unpick the complex interrelationships between data elements. Sometimes, Big Data is the vehicle by which enough data is gathered together about enough aspects of enough things from enough places for those interrelationships to become observable against the background noise. Other times, Big Data is the background noise, and any hope of insight is drowned beneath the unending stream of petabytes.”
