Remembering Big Data Gravity – Part 1
If you’ve wondered why so many companies are eager to control data storage, the answer can be summed up in a simple term: data gravity. Ultimately, where data is determines where the money is. Services and applications are nothing without it.
Dave McCrory introduced his idea of Data Gravity with a blog post back in 2010. The core idea was – and is – Interesting. More recently, Data Gravity featured in this year’s EMC World keynote. But, beyond the observation that large or valuable agglomerations of data exert a pull that tends to see them grow in size or value, what is a recognition of Data Gravity actually good for?
As a concept, Data Gravity seems closely associated with current enthusiasm for Big Data. In addition, like Big Data, the term’s real-world connotations can be unhelpful almost as often as they are helpful. Big Data exhibits at least three characteristics, which are Volume, Velocity, and Variety. Various other V’s, including Value, is mentioned from time to time, but with less consistency. Yet, Big Data’s name says it’s all about size. 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.
On its own, the size of a data set is unimportant. Coping with lots of data certainly raises some not-insignificant technical challenges, but the community is actually doing a 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 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.
To a degree, Data Gravity falls into the same trap. More gravity must be good, right? And more mass leads to more gravity. Mass must be connected to volume, in some vague way that was explained when I was 11, and which involves STP. Therefore, bigger data sets have more gravity. This means that bigger data sets are better data sets. That assertion is clearly nonsense, but luckily, it’s not actually what McCrory is suggesting. His arguments are more nuanced than that, and potentially far more useful.
Instinctively, I like that the equation attempts to move attention away from ‘the application’ toward the pools of data that support many, many applications at once. The data is where the potential lies. Applications are merely the means to unlock that potential in various ways. So maybe notions of Potential Energy from elsewhere in Physics need to figure here.
But I’m wary of the emphasis given to real numbers that are simply the underlying technology’s vital statistics; network latency, bandwidth, request sizes, numbers of requests, and the rest. I realize that these are the measurable things that we have, but feel that more abstract notions of value need to figure just as prominently.
So I’m left reaffirming my original impression that Data Gravity is “interesting”. It’s also intriguing, and I keep feeling that it should be insightful. I’m just not — yet — sure exactly how. Is a resource with a Data Gravity of 6 twice as good as a resource with a Data Gravity of 3? Does a data set with a Data Gravity of 15 require three times as much investment/infrastructure/love as a data set scoring a humble 5? It’s unlikely to be that simple, but I do look forward to seeing what happens as McCrory begins to work with the parts of our industry that can lend empirical credibility to his initial dabbling in mathematics.
If real numbers show the equations to stand up, all we then need to do is work out what the numbers mean. Should an awareness of Data Gravity change our behavior, should it validate what gut feel led us to do already, or is it just another ‘interesting’ and ultimately self-evident number that doesn’t take us anywhere?
I don’t know, but I will continue to explore. You can contact me on twitter @bigdatabeat