Despite its title this paper is not really about analytics. That is to say, we are not going to discuss analytics per se. What we are going to consider are the extraneous factors that might influence decisions about providers of analytics, within the context of the Internet of Things (IoT). These arise as a direct result of the distributed nature of IoT environments. This can be best understood – in our opinion and from an analytic point of view – as a concentric series of increasingly complex and sophisticated capabilities. We had better explain what we mean by that.
The Internet of Things and its derivatives (Internet of Everything, Industrial Internet of Things, Industry 4.0 and so on) consists – simplistically – of sensors, gateways and some sort of central processing area (or hubs). Except that there may be more than one layer of intermediate processing between sensors and central systems and, for that matter, there may be more than one layer of central processing. Looked at from this perspective a connected car – considered as an IoT device – is simply a series of sensors with one or more gateways to aggregate and filter data from those sensors. An autonomous car goes further by including its own central processing capabilities. Indeed, it is arguable that an autonomous car represents an entire IoT ecosystem in its own right. The fact that it intercommunicates with other cars,
with the motor manufacturer and with smart cities means that you can have subsets of IoT that are themselves IOT environments.