M2M Conference Keynote: The Next Big Thing in the Internet of Things

This week in Miami, FL, our CTO & Co-Founder, Dr. Dale Skeen, was keynoting at the M2M Evolution Conference. Machine-to-machine (M2M) communications is a subset of the Internet of Things (IoT) and a market of markets, where each of the markets is growing exponentially. The title of Dale’s keynote was “The Next Big Thing in the Internet of Things: Real-Time Big Data Analytics,” but essentially his keynote was about “Real-Time Analytics” and its importance to IoT, and why you should care.

Real-Time Analytics over enormous amounts of Big Data that is streaming in from all connected devices that comprise the IoT network will generate big value with a big impact. Of all the big numbers being thrown around about IoT – these are my favorite:

  • $15 trillion – the economic value expected to be generated by IoT by 2030. To put this number in perspective, this is almost the GDP of the US and is 20% of the current GDP of the entire world.
  •  30-40% ($5+ trillion) – the value attributable to analytics.

 

Now, we know that IoT generates an enormous amount of data – truly Big Data. And when people think about Big Data, they tend to think about “lakes,” and capturing this data into a Big Data lake. However, for time-critical IoT, the story is not about lakes…it is about streams of data coming-off of smart infrastructures and IoT networks in near real-time, and whose value quickly slips away with time. This means that we need to process it as data streams, in real-time, using new emerging “Streaming Analytics” tools, which are capable of handling fast, streaming Big Data.

Streaming Analytics differs fundamentally from traditional (lake) analytics, as summarized here:

In summary, Real-Time Streaming Analytics is fast because all processing takes place in-memory, it is continuous because it updates the analytics on every new data item (millions of times-per-second), and it can do this efficiently because it uses incremental algorithms (which is the “Secret Sauce” of Stream Processing). It is these capabilities that makes Streaming Analytics ideal for IoT.

In the subsequent blog post, I will describe how Streaming Analytics for IoT drives value using a couple of real-life use cases to illustrate.

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