Network Infrastructure

Big Data and the Internet of Things

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The Internet of Things has the potential to drive incredible innovation in the way we live our lives. That I can know about remote things, like smoke detectors, and security cameras, and healthcare devices, and cars, wherever I am in the world, is really useful. That these things can have programmed understanding to react is really useful too – so smoke detectors can call the fire brigade, pacemakers can call the medic, and security cameras can call the cops. But we’re really only at the beginning of an appreciation for what the Internet of Things is capable of, and its secrets lie in data.

When communications service providers come to IBM and ask us about the Internet of Things, it is increasingly the case that they are talking first about data. Sure, we’re working with them all over the world to facilitate machine to machine infrastructure, service management and all that core connectivity stuff. It needs to be solid, robust, secure. But the really interesting conversations begin once we accept that infrastructure as just the foundation. Taking a deep breath, with millions of devices of connected, and millions, billions more in the queue, we ask the question: now what?

There are dedicated security applications – like security cameras – but what can the data yield from the wider internet of things teach us about security? Can traffic patterns and weather patterns and WiFi patterns and lighting infrastructure yield hints about potential risks? In healthcare – clearly health and fitness applications can teach us about personal health, but what can we know based on travel patterns, purchase history, demographics and local data? Can percentages and scoring based on wider integrated variables help us to better understand security and health risks?  There are clearly other applications of this wider data sweep in the commercial realm – buying propensities, fraud / default propensities, and all manner of other models that can benefit from the Internet of Things to make business work better too.  So how do we get there?


There are three data groups to consider. First, the client data – so, if a service provider is delivering connectivity to an auto company, for example, there is the data yield from the cars. The location, telemetry, interactions (in-car entertainment, air conditioning for example) data. The second is the bearer, or platform data – in this case the telecommunications network, including network performance data, service consumption data, call patterns, messaging data, and so forth. And finally there is third party aggregated data – from a partner like IBM, perhaps, with partnerships with Twitter and The Weather Channel, or data made available through our UBX platform for data analytics.

The next stage is application – how does one expose that kind of data, insight and analytics? How does it get applied in the real world? Deriving great insights from the Internet of Things, and layering intelligence on top is clearly of some value, but how do we build a business based on it? We’ll look at that in a follow-on post in a couple of weeks time.

Digital Transformation Segment Leader, IBM Global Telecommunications Industry

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