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From the Internet of People to IoT and the Analytics of Things

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Take a look at where IoT has been and where it’s going for providers and users alike. See how the world is shifting around IoT and how data analytics will play a supreme role.

If you follow Gartner, Forrester, and other industry analysts, you’re probably a reader of Gartner’s ‘Hype Cycles’ and Forrester’s ‘Waves.’ According to Gartner, IoT is at the top of the Hype Cycle, and is forecast to deliver economic value of £1.2 trillion by 2020 ($1.7 trillion USD, for those of you who like to cover all the currency bases). Thus it should be obvious to all of us that the Internet of Things has taken the place of Big Data in the Hype Cycle.

I’d like to go further, using a few examples from industry, to discuss how important IoT really is, and how operational systems in the IoT are informing what I call the ‘Analytics of Things’. This trend is creating an emerging business model, which will lower the cost and risk of the IoT business model.

First, let’s level set. In my view, the Internet of Things is an expansion of the notion of the Internet of People — the Web — the Internet.

People were the first beneficiaries of the Internet. The first real use cases were the creation of communities, content sharing, and, of course, e-commerce. We also created lots of data with cat videos and baby pictures, emails, and social posts.

Now, as we head into the third age, the digital age, millions of devices and machines are connected, and these things, and the tiny chips embedded in them, are able to communicate their status with each other, and with headquarters — whether that’s a central machine, a cloud computing instance, or a corporate headquarters IT department.

They do this through a series of aggregation points, or networking devices called gateways, which aggregate and package up data to send onward. The networks themselves convey this sensor data, either to public clouds or private data centers, to enable people (and machines) to do analytics on the machine and sensor data.

What really worries companies is the prospect that this growing volume of data is MUCH bigger than when it was just the Internet of People and their devices. Sensor data is probably a couple of orders of magnitude bigger than web log data and consumer interaction data. The data produced by the IoT is doubling every two years, and this creates fear among businesses trying to budget to manage that data — storage, analytics, compute resources, and the complexity and execution and opportunity costs of that data — and the risks of what if I don’t get started fast enough? What kind of analytics will be done? At what speed and where will it be done?

A distinction I want to make is that there are really two subsystems here. The first is where the operations among these things occur and where the thinking about these operations is done. The ‘operations of things’ in the operational network where the devices live, where the gateways live at the edge — that’s where the actions and decisions are made that inform what businesses do with all that data. The second subsystem is where the thinking about these things and the analytics is done. If you, as I do, believe that thinking should precede acting, that’s a simple way of thinking about how analytics needs to power the operations of things. To be complete, there is also a third domain, where we close the loop and take action, orchestrate and execute those actions back into the operations of things.

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