Diving into data gravity: Context is everything

Movies such as Interstellar and The Martian have not only taken the box office by storm, they’ve also taken a look at what life in deep space is like. It’s fascinating to explore the unknown–there’s plenty out there we still have to learn, and every day we’re shining new light on various areas.

Much like the endless expanse of space can teach us many things, the cloud can provide great knowledge, as well. We’ve touched on the idea of data gravity previously, something Dave McCrory proposed a few years back. To recap:

Consider data as if it were a planet or other object with sufficient mass. As data accumulates (builds mass) there is a greater likelihood that additional services and applications will be attracted to this data. This is the same effect gravity has on objects around a planet. As the mass or density increases, so does the strength of gravitational pull. As things get closer to the mass, they accelerate toward the mass at an increasingly faster velocity.

There are numerous reasons why an enterprise may not want all of its data in one place. Whether it’s privacy, compliance, or cost, the desire to have data in both a data center and on the cloud makes sense. However, having all of the data in one place will allow you to more easily understand the context of your data. Moving data to the cloud allows its analysis to happen in the same place as where it was created, and our partnership with DataStax simplifies our ability to provide detailed analysis of massive sets of data in real-time, regardless of cloud environment.

It’s no secret that, without data, services and applications lose a lot of their usefulness. But having the context of that data is helpful, as well. Consider an image or video taken from a camera–if the file name was just a random string of letters and numbers, it would be hard to tell what kind of file you had, simply by looking at the name. However, if the file name were something like “DSC — 007,” you’d logically conclude it was from a “Digital Still Camera,” and it was the seventh file uploaded from that camera. As the cloud continues to grow and software continues to get smarter, we can infer more and more from a piece of data. We’ve already seen this with things like a file as it is created, modified, or accessed.

Per McCrory’s model, the closer you are to data, the higher the throughput and the lower the latency to the data. Those applications and services will then become more dependent on low latency and high throughput. Again, having the context in which to utilize that data is extremely important. In our work with McDonald’s, they strived to ensure they reached their tech-savvy, always on customers, and engage them in the way they want to be engaged. With 70 million customers served every day, that’s a tremendous amount of data coming in. McDonald’s couldn’t simply have a static environment; they needed a reliable, nimble, scalable model. With our ability to analyze data at a much faster rate, it made sense for McDonald’s to move outside of their data center walls into an AWS environment. Using our analysis, they now can piece together greater context around their data, which will allow them to even better serve their customers.

It can be a little nerve-wracking moving your data from your own data center to the cloud, but it doesn’t have to be. Naysayers will insist the pain of migration is too much, but that’s hardly the case. You’re still in control of the data you choose, and in many instances, the agility, flexibility, and cost efficiency are all well worth it — we’ve seen those kinds of positives happen quite often. Of course, you’ll need to decide what’s best for your business and its goals.