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Analytics Of Things -- What Does It Mean, And Where Is It Taking Us?

July 15, 2015   |   9:19 PM
Big Data Analytics
Teradata Articles

Not too many years ago, “big data” was a new and unfamiliar phrase that cropped up before anyone really defined it.  We never did reach consensus on a precise meaning, even as the term slipped into common parlance and big data has now transformed what’s possible in business and society. Some people still fuss over an exact definition; but as I’ve argued here before, that’s a moot point.  We may not have an exact meaning; but we know what big data has come to mean for our economy and for our world.

Much the same happened more recently with the “Internet of Things.”  Definitions remain muddy, but most readers have come to understand we’re talking about data from a growing universe of sensors and machines. And most people know this data is hugely valuable; Gartner and others have anointed the IoT as a top strategic trend in technology. The “things” include everything from environmental sensors monitoring weather, traffic or energy usage; to “smart” household appliances and telemetry from production-line machines and car engines. Those are just a few examples.

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Now there’s an even newer concept out there: what I call the “Analytics of Things.”  Like the nifty terms that came before, however, finding an exact definition is not a big concern for me. Let’s instead go right to the fun part and consider the new possibilities that the AoT enables – for myself and other analytics practitioners working to create next generation analytics, and for the rest of the world as we begin to see the innovations the AoT is bringing about.

With AoT, we’re essentially talking about the kind of analytics that become possible with algorithms and data connections now that billions of sensors and all those other “things” are out there. What’s most interesting to me is the role of connections:  What kind of analytics become possible when connecting all these IoT sources? Also, what can analytics allow these things to do all by themselves – even if they’re not connected?

As we build better algorithms for the burgeoning IoT digital infrastructure of sensors and machines that are now indexing our world on a granular level, we can use connection-based analytics to get very proactive in predicting future performance and conditions (like auto industry recalls) and even prescribing future actions (like design change suggestions for future models).  At the same time, analytics can make each “thing” more self-sufficient in adjusting and “deciding” how to operate based on changing conditions, even if that “thing” is operating all by itself.

Kudos to Thomas Davenport for his accessible example here of the modern household thermostat. When connected to the larger digital world, such smart thermostats let you do things like adjust and monitor temperature remotely and optimize energy usage patterns across the power grid. But on its own, when not connected, each device has embedded analytics that help it detect when people are in a room, what times they’re likely to be in a room (based on previous observations) and decide, all by itself, to adjust temperature accordingly.

I believe that the dynamic between IoT and AoT represents a very exciting interplay that will fuel progress – a kind of stepwise dialogue between devices and analytics that is driving our evolution toward more complex systems and capabilities.   New devices create potential for new analytics; and these new analytics create new possibilities for device creation and modification, as well as what’s possible whether they’re connected or acting alone. The “whole” of this innovation potential, so to speak, is greater than the sum of its IoT and AoT parts.

I’ll talk more in later blogs about some impressive use cases we may see in the future. But, it’s already clear how quickly the Internet of Things and Analytics of Things are taking us there. What value is all of the data from the IoT if we aren’t focused on producing analytics from it via AoT?