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Elluminance aims to solve Industrial IoT big data challenges

Big data for the Industrial Internet of Things is far different from big data for the Internet of Things, namely because what takes seconds — or more — must now take microseconds or less.

To overcome the time constraints associated with Industrial IoT and provide real-time monitoring, gathering and analyzing of IIoT data, elluminance Monday announced new product set that should help organizations capitalize on the insights derived from physical and real-world events, sensors and actuators, instrumentation, and software/IT infrastructure.

The Austin, Tex.-based company’s Real-time Data Platform and Time to Insight are deployed on National Instruments technology — which uses field programmable gate arrays (FPGAs) — and backend technology runs on Hewlett Packard Enterprise’s Moonshot development platform.

“The feedback data from sensors is under-utilized,” said Barry Hutt, elluminance CEO. The company’s diagnostic, predictive and prescriptive technology, he continued, are critical to harnessing the value of real-time industrial data to, for example, prevent failures in industrial engines or build a smart grid that can automatically detect and respond to issues (such as a crack in a utility pipe) or seamlessly put energy back onto the grid.

“I could just predict a behavior,” Hutt said, “But before I even know I have a problem, I’ve already fixed it by deriving from lots of different sources of data.”

Venture-backed elluminance, which was founded in October 2015, is able to do this as it is comprised of experts from various backgrounds — including sensors, instrumentation, IT and hardware — all places data is derived from.

This team of experts also knows that real-time computation and algorithms are two of the most costly barriers of IIoT analytics.

“Real-time doesn’t just mean fast,” said Darren Schmidt, chief technology officer of elluminance, “It means reliable and stable.”

Elluminance is prepared to help every step of the way — from sensor to backend. Its technologies connect data with the appropriate sense of real time — be it with FPGAs, CPUs or GPUs — as well as the proper algorithm which can pull value from operational technology. The company works with clients to map out the problem, response time (be it categorized “slow” at 10+ seconds or mission critical at 20 μs) and problem size, and then connect it with the proper algorithm, execution environment and data management solution.

“As the new networks link data from sensors to IT analytics systems, new and compelling insights and knowledge will be revealed, and those will boost operational efficiencies. The wisdom gained will enhance decision making and transform businesses,” said Hutt in a press release. “Our roots run deep in the technologies that are driving the growth of the Industrial IoT. Our customers reach new understandings of complex real world problems that can lead to ground-breaking solutions in months instead of years.”

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Great insights on the benefits of Speech Analytics for call centers. We have built a role-play simulator that is being used by call centers and just recently added a speech to text translator so we can compare what the agent said in the simulation to a key word search database that will score the conversation based upon the agent using the key words expected. Now we can see where this same technology can be used to collect large volumes of data to compare what is being said over a period of time to create better scripts.
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There are obvious benefits to speech analytics. Much to learn and tailor for the benefit of the consumer and, presumably, the profit center behind it all. Yet this advances comes with a nagging sense of 1984, that imaginary time when manipulation is the norm. Oh, wait, not so imaginary at all.

The key question should never be "is it good for business" but "is it good for the consumer first and business next."  But I doubt we'll get to that.
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