This article appeared at insidebigdata.com
At its core, the Internet of Things is about sensors embedded into devices of all kinds, which provide streams of data via internet connectivity to one or more central locations. The purposes for transmitting sensor data are myriad, but the assumption in all cases is that that data can then be analyzed and acted upon in some way that is beneficial to the user. In short, this means that all IoT-related services, no matter how disparate they may be, always follow these five basic steps:
The birth of IoT
If I ask myself how the IoT came to be, the shortest answer I can provide is that good ‘ol Moore’s Law made the first three steps in this chain (Sense, Transmit, and Store) ubiquitous and commoditizable. The hardware, software, and connectivity required to perform these steps has become very small, very cheap, very efficient, and very broadly available. When we hit the point of critical mass a few years back when all of those “verys” became applicable qualifiers, the IoT was born. It’s critical to note here that these first three steps were subject to this commoditization because, essentially, they are relatively “dumb”: long-existent capabilities coupled with standard coding make them all possible.
It takes AI to take action
However, for any IoT application to be worth buying (or making), it must demonstrate value in the last step of that chain, the “Act”. Of course, “act” can mean an infinite number of things, ranging from a profound physical action (e.g. deploying an ambulance to the site of an auto accident) to merely providing basic information to a relevant consumer (e.g. sending a text message to alert a driver that their car needs an oil change). But no matter what the ultimate step of “Act” actually is, its worth is entirely dependent on the penultimate Analysis.
It is here, at the “Analyze” step, that the true value of any IoT service is determined, and this is where artificial intelligence (or, more properly, the subset of AI called “machine learning”) will provide a crucial role.
Machine learning makes actions valuable
Machine learning is a form of programming that empowers a software “agent” with the ability to detect patterns in the data presented to it so it can learn from these patterns in order to adjust the ways in which it then analyzes that data. We already experience benefit from machine learning in our everyday lives when Netflix gives us a tailored movie recommendation or Spotify modifies our playlist. When machine learning is applied to the “Analyze” step, it can dramatically change what is (or is not) done at the subsequent “Act” step, which in turn dictates whether the action has high, low, or no value to the consumer.
In the next installment of this blog I will illustrate the major differences between IoT services that employ Machine Learning and those that don’t, and what this means to one’s chances for monetization success in the IoT.