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When first introduced back in the 1970s, truck fleet operators used basic GPS-based telematics solutions to remotely monitor the location and speed of their truck tractors and trailers in real time. Many still do, and continue to realize good ROI from these solutions. While this represented an early example of a Big Data problem, analyzing that data was relatively straightforward.
Now fast-forward to today’s far more sophisticated telematics solutions for truck fleets and other mobile assets. In some cases, these go well beyond GPS location and speed monitoring and analysis to also incorporate real-time measurements from engine sensors (to help improve fuel economy and support predictive maintenance), cab-mounted cameras (to monitor driver performance), and other measurements from vehicles that could be spread across continents.
These multiple disparate data sources, often involving sub-systems from multiple suppliers, increase the Big Data problem to a significant degree, and can make analyzing these data far more challenging. However, “enriched” data can provide fleet operators with valuable predictive insights to help improve fleet safety, reliability, and regulatory compliance, while reducing overall operating costs.
ARC is confident that this same “enriched data” approach can be applied across a variety of industrial sectors to yield similar benefits.
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Keywords: Big Data, Predictive Analytics, Telematics, Fleet Safety, IIoT, ARC Advisory Group.