The Internet of Things (IoT) today connects multitude of devices through the Internet, effectively integrating greater compute capability, and also using data analytics to extract meaningful information. With over 50 billion connected devices and 212 billion sensors available today, it has tapped into the opportunity to deploy new intelligent devices. Owing to its massive adoption, the cost of sensors, bandwidth, and computer processing are going down. These trends have unleashed the IoT potential, impacting the way we currently work and live. Such next-generation intelligent systems are collecting and analyzing large volumes of raw data, enabling manufacturers to act upon the results to reach new levels of factory automation. Prediction of new events from big data provides a concrete foundation for planning new projects, but all new insights may not be workable or interesting out of million events. So, revealing these meaningful insights is a challenge for data scientists to write suitable algorithms.
Additionally, considering connected car as a prototype for predictive analytics in industrial big data, analyzing the error messages generated by these cars provide the manufacturers with useful insights that assist in optimizing service and production of vehicles. This leaves manufacturers, dealers, exporters, selling agents, and service providers in automobile industry with heaps of data every day. Integrating and analyzing this big data assist for streamlining the productivity, product quality enhancement, market demand, customers’ interest of a specific model, and cost of vehicles. In an Industry 4.0 context, the collection and comprehensive evaluation of data from many different sources—production equipment and systems as well as enterprise and customer-management systems—will become standard to support real-time decision making.
