The Internet of Things (IoT) has arrived, and the promise of a connected-device world is taking shape as innovative companies bring improvements to market in the form of automated water meters, connected industrial machinery, and useful consumer products, to name just a few categories. With the current estimated number of connected IoT devices reaching well into the billions, there is still tremendous room for growth to the projected 50 billion devices expected to be connected to the IoT by around 2020.
If you are embarking on an IoT project and you are reading this, you already have a sense that the data generated by your devices is valuable and must therefore be captured, analyzed and leveraged for your (and your customers’) benefit. This is true. You are also likely to see a conflation of the following concepts: IoT, Big Data, and Analytics. I guess that makes sense to some degree; billions of connected devices are going to generate big amounts of data, so clearly a big data solution is needed, yes? No.
Most people are familiar with the concept of analytics: At it's most basic, analytics is about finding meaningful patterns in data. That sounds like a smart thing to do, and it is, because the meaningful patterns in your data generally represent some kind of opportunity.
For instance, you might be trying to figure out whether the button on your website should be green or red. Make the button green for a few days, then make it red for a few days and compare the patterns in your data to see which one gets clicked on more. (It turns out it’s red.) This sort of A/B testing is among the simplest of analytics exercises, and looking at a relatively small amount of data can yield astounding insights that can lead to impressive results if acted upon.