100M Sensors under Management: C3 IoT Emerges from 8 Years in Development 

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After eight years in quiet development under the stewardship of Silicon Valley veteran and CRM pioneer Tom Siebel, C3 IoT’s “full stack” IoT Platform-as-a-Service has created a stir since coming out of stealth earlier this year. In fact, it’s a company that industry analysts say may have stolen a march on competitors in the nascent IoT/big data predictive analytics solutions industry.

Signs point to strong potential. For one, the company already has nearly two dozen large-scale IoT analytics implementations under way, in the U.S. and in Europe. C3 IoT says it has more than 100 million sensors and devices under management, making it the world’s largest provider of large-scale big data, predictive analytics and IoT applications, according to Siebel.

For another, Siebel and C3 IoT hit the market with pedigree and credibility. He and many of his C3 IoT cadre, about 50 engineers, have worked together at Oracle and then at Siebel Systems, building it into a $2 billion CRM market leader before Oracle acquired it for $5.8 billion in 2006. That track record helped attract $200 million from individual and institutional investors while enabling Siebel and colleagues to leverage existing customer relationships with Fortune 500 companies willing to listen to the C3 IoT technology story.

In addition, the company claims Amazon Web Services as its largest marketing partner, with C3 IoT offered as a cloud-based platform built on AWS and with the two companies engaged in joint sales deals.

Solvency is another positive: last month, C3 IoT reported a year-over-year revenue increase of 65 percent and a bookings increase of 600 percent, and that it’s cash-positive for FY 2017.

Taken together, C3 IoT competitors “are likely at least 2-3 years behind C3 IoT from a product development stand point, while all of C3 IoT’s competitors have a huge amount of catching up to do in terms of customers and devices under management,” according to a report issues last year by Harbor Research. (For more information on the company, see “Q&A with C3 IoT’s Tom Siebel” in sister publication Datanami.)

At the heart of C3 IoT technology stack is the C3 Type System, designed to overcome the Babel-like interoperability complexities inherent in large-scale IoT implementations.

The C3 Type System is a data object-centric abstraction layer that binds together the various C3 IoT Platform components, including infrastructure and services. It allows programs, algorithms, and data structures – written in different languages, with different computational models, making different assumptions about the underlying infrastructure – to interoperate without knowledge of the underlying physical data models, data federation and storage models, interrelationships or dependencies.

According to the company, the C3 Type System lets developers and data scientists focus on delivering big data, predictive analytics and IoT applications without the need to integrate the complexities of the underlying systems.

C3 IoT’s “a data-first approach, allow(s) enterprises to bring together all data on a cloud-based system,” according to Holger Mueller, VP and principal analyst, Constellation Research. “C3 IoT transfers data into a type system-based platform, where data is immediately filled with semantic meaning and intrinsic functionality. C3 IoT has adopted an industry approach to the type system, allowing customers to start using the platform with meaningful vertical functionality, right from the start.”

The industry first targeted by C3 IoT was utilities (the company has since expanded into manufacturing, healthcare, aerospace, financial services, and oil and gas), with its high numbers of machine-addressable devices – meters, thermostats, transformers, substations, and so on.

Among its utility customers in Europe are Engie, the French multinational nuclear energy electric utility provider and Enel Distribuzione, with more than 30 million smart meters in Italy. Enel uses C3 IoT for predictive maintenance in 16,000 power substations. According to Enel, the C3 IoT platform integrates data from various sources (historical data of equipment failures, maintenance work history, ground and plant morphology, weather), enabling system updates on the status of the grid in real time. Potential breakdowns can be predicted and localized in a few seconds, allowing Enel to carry out inspections and solve problems proactively.

In the U.S., Con Edison (electricity and natural gas provider for the five boroughs of New York City and Westchester County) has announced adoption of the C3 IoT platform for a multi-year undertaking that includes replacement of 5 million old-school electromechanical meters with new sensor-equipped smart meters. The Advanced Metering Infrastructure (AMI) project is part of a larger effort to understand and predict equipment and network performance/failures, reduce operating costs and better understand customer behavior – with the overall goals of lowering operating costs, cutting energy use and maximizing revenue.

Con Edison’s intent is to aggregate and keep current enormous volumes of disparate data – including telemetry from sensors and devices, data from diverse enterprise information systems, and external data sources, such as weather, traffic, and social media – into a single cloud-based data image. It will employ advanced analytics and machine learning at scale, in near-real time, “to generate business insights that improve operations and enhance customer engagement,” the utility said.

The companies estimate data volumes will exceed 115 terabytes, growing by 104 gigabytes per day, from 480 million meter reads each day.

“We’re going to be implementing different types of analytics to see what we can do to conserve energy,” Jamie Prettitore, Con Edison’s director, Advanced Metering Infrastructure, told EnterpriseTech. “It will allow us to more efficiently operate the power grid. If we can reduce energy delivery throughout the system by 1.5 percent - that may not sound like much - but it adds up to several hundred million dollars over 20 years and a fair amount of carbon reductions as well.”

Con Edison will leverage the C3 IoT AMI Operations, employing self-tuning machine learning algorithms to identify meter and network performance issues, to track installation and deployment issues and for predictive maintenance.