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.
Bigger sets of data translate into greater derived insight because of additional possible correlations. There are some big websites. Can you imagine how much data Amazon generates from its millions of daily transactions? Can you imagine how they are using that data to increase sales? How big is big data? IBM estimates that every day there are 2.5 billion gigabytes of data created in the world, and somewhere, someone is crunching some of that data to gain some valuable insight.
"WHEN HUMAN TRAFFIC OVER THE INTERNET IS THE SMALLEST FRACTION OF DATA, TRANSACTIONS BY DEVICES WILL EASILY REACH INTO THE TRILLIONS."
Collective Intelligence in IoT Analytics
The Internet of Things is about to dwarf the current pace of data transactional creation. There are 2.7 billion people connected to the internet today, and we already have more connected devices than people. By 2020, Intel estimates there will be an average of 7 connected devices per person. Ericsson is projecting more than 50 billion connected devices comprising the Internet of Things by 2020. When human traffic over the internet is the smallest fraction of data, transactions by devices will easily reach into the trillions.
Just like A/B testing a button color on a website can measurably increase usage, engagement and sales, so can IoT Analytics have a similar – and potentially much greater – effect. Consider the fact that “connectedness” is the ingredient underlying much of the Internet of Things and you start to see the untapped opportunity that exists. A Coffee Maker isn’t a Thing, but a connected coffee maker is. The manufacturer of a connected coffee maker probably has decades of experience making coffee makers, but until it was connected to the internet, the manufacturer had no way to gain valuable insight into how their millions of coffee makers are being used, in real time. All of a sudden, a treasure trove of data is being generated if only that manufacturer can tap into it, properly analyze it and then act upon it.
When do people use coffee makers? How much do they make and consume? Which flavor cartridge is the most popular? Does it differ by geographic location? How about by demographics? Can all this information be used to improve products through applied R&D? Yes. Can it be used to drive sales of related consumables and accessories? It sure can. Can this valuable data be used to improve customer service by calling up usage history on a particular unit to help diagnose problems? Absolutely. But IoT Analytics goes far beyond even those examples.
What if a thousand coffee makers in a close geographic proximity fail at once and your Tellient IoT Analytics engine tells you it is because there was a strong earthquake in that area and those coffee makers literally jumped off the counter to their deaths. What can that manufacturer do with that information? The manufacturer will know that they do not have a warranty liability because the unit broke due to force majeure, and on top of that, they will have a consumer-pleasing sales opportunity to give their unfortunate customers an electronic coupon for a discount on a new unit.
Before the Internet of Things, all manufacturers could do was guess at how their products were actually being used. IoT Analytics will allow manufacturers to stop guessing and know how to make better products.