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How to Leverage Cloud for developing & building on IOT – Talking Cloud With Azure Part 2

How to leverage cloud for IOT


Giovanni Tropeano:  So, another area that a lot of businesses are looking at is basically how to leverage the cloud for developing IOT and building on IOT, any thoughts there?

Karl Rautenstrauch:  Yeah, yeah boy we have those conversations constantly with our customers.  Whether it’s a connected car project or a proactive maintenance type of project, we’ve got a very high profile public case study with ThyssenKrupp about what they’re doing with IOT and their elevators that are deployed around the world.  And really IOT is all about aggregating very quickly massive amounts of data. Massive amounts of central data and running that through advanced analytics and machine learning algorithms to find patterns that we necessarily wouldn’t with traditional means or with just man power, right looking over maintenance schedules.  So, those are two examples of what we see, but yeah IOT is becoming pervasive. I look around my office here and I’ve got a ridiculous number of connected devices in this office right.

My lights turn on with voice activation thanks to my assistant over here on the desk, you know my printer is connected it’s a cloud connected printer, I can print from anywhere in the world, not to mention the laptop, the tablet, the desktop everything else that’s sitting here in just this small space.  So this is an area that’s exploding and it’s a great example of where customers have an option. Do you build everything you need to support and create that IOT infrastructure or do you leverage what’s been built and what’s available on a public cloud platform like Azure and I think what we’re finding is increasingly customers are turning to us for the core.  We are the repository for all of that and they are focused on the edge aspects of it, the edge processing and edge network based aspects that have to live closer to them, closer to their clients and where they need to aggregate that data from.

Jay Livens:  Yeah it’s a classic challenge of massive scale.  You know it’s just that data just keeps growing and keeps growing and arguably as it gets bigger and bigger the potential for analytical sort of time series way to benefit gets greater.  But you know having to manage and store and analyze that huge quantity is a real challenge and particularly you know with it always growing it’s an environment I think where the compute flexibility, the storage flexibility, the sort of time series optimized nature of some of the services offered really make a lot of sense.

Karl Rautenstrauch:  Absolutely. It’s a queuing infrastructure that’s already in place it’s the analytics and the artificial intelligence that’s already in place, you don’t have to go out and build it on your own.  Leverage our platform and that’s exactly the direction we are seeing customers go.

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