Internet of Things platform supplier C3 IoT this week announced two sweeping contracts, one with ENGIE, a huge energy company in Europe, the other with the U.S. Department of State, adding to the eight-year-old company’s roster of big IoT wins.
The Department of State is said to have signed a multi-year deal valued up to $25 million to use C3 IoT’s enterprise application development platform for a global energy management initiative. C3 IoT will enable the Department of State to “gain dynamic, real-time operational insights and efficiencies by analyzing … data from enterprise and extra-prise systems and sensors across 22,000+ Department facilities in 190+ countries,” C3 IoT says.
ENGIE, which is a 70 billion euro, French-based, multinational energy company, plans to use the C3 IoT platform as one of the main planks in a digital transformation initiative, says Yves Le Gelard, who is CIO of the company but was also recently made Chief Digital Officer (“I take my tie off for that role,” he laughs).
These two large contracts add to the impressive IoT accounts C3 IoT has won to date. “We already have 20 large scale deployments worldwide,” says Chairman and Chief Executive Officer Thomas Siebel, formerly CEO of $2B Siebel Systems, which merged with Oracle in 2006. “The largest is a utility grid operator in Europe called Enel that has 61 million meters in 40 countries.”
C3 IoT describes itself as a software company that delivers an enterprise IoT development platform and applications. “It is a development environment that allows organizations to develop applications that aggregate data from enterprise information systems (ERP, CRM, factory floor automation systems, all that stuff), extra-prise information systems (weather forecasts, temperature, social media, etc.), and sensor networks,” Siebel says. “We pull all that together and build a unified federated image which can be petabytes in scale and growing at 100s of gigabytes or terabytes per day.”
That’s hard enough, but then the trick is making sense of all of that. “You have to apply machine learning to engage in a practice called predictive analytics,” Siebel says. Predictive analytics, in the case of electrical grid operator, will use machine learning to discern “what device is likely to fail next, why is it going to fail, and what is the probability of failure,” he says.
For Le Gelard, knowing in advance that a component in one of the company’s 400 massive wind turbines is about to fail would be “just unbelievable. We manage very large power plants, pipelines, energy terminals, huge billion dollar assets, so saving even 1% in uptime is huge.”
Asked what IoT means to an industry that already uses so many networked sensors, Le Gelard says, “What is new is the ability to correlate and cross analyze the industrial stuff coming from SCADA networks, from machines, with unstructured data from other systems and sources, and through machine learning techniques, improve and predict things. You couldn’t do that before because of the sheer size of the data.”
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