New generations of connected machines are joining the world's networks at an astounding pace. Broadly called the "Internet of Things" (IoT), these devices and associated technologies would be tied to local and global networks through embedded sensors that are “always on.” The number of devices connected to, communicating through, and building relationships on the Internet has exceeded the number of humans using the Internet. Between 2011 and 2020 the number of connected devices globally will grow from 9 billion to 24 billion as the benefit of connecting more and varied devices is realized. The data produced by the Internet of Things, sometimes called industrial data, is as diverse as the machines themselves – including those from logs and sensors, but also sources such as power grids, health monitors, security cameras, computer networks, call detail records, financial instrument trades and more.
The way we live and interact with everyday things are being radically personalized by the convergence of Big Data, advanced analytics and the Internet of Things.
The biggest question is how organizations, governments, individuals would leverage the vast amount of machine to machine data that is being emitted by the connected devices and how to make best use of that information.For example: a blade in a gas turbine used to generate electricity creates 520 Gigabytes of data per day. And there are 20 blades in each turbine. An airplane on a transatlantic flight produces several terabytes of data, which can be used to improve safety, streamline maintenance operations and decrease fuel consumption. The amount of data aggregated over weeks, months and years, are astonishing. This type of data automatically generated by the machines and equipment’s are going to become a fantastic natural resource. The application of predictive analytics on such Big Data has the potential to make companies smarter, more progressive and provide them a competitive advantage.
Companies are using advance statistical modeling techniques to analyze the sensor data and provide real time insights on event correlations, root cause analysis, forecast potential risks and simulate possible scenarios. Machine learning techniques can help us extract and discover patterns and insights from these vast mounds of data. Advanced analytics is poised to become an integral part of business processes to trigger intelligent decisions in real time and changing the way we live our lives, the way we conduct our businesses or the way government machinery functions. Gartner predicts that by 2017, more