Internet of Things data analytics gives insight m2m solutions
A variety of value-adding, insight-generating and decision-enabling data emanating from a variety of geographically distributed sources—which, incidentally, are increasingly common—are in need of comprehensive, yet cognitive, analytics. Thanks to a sharp increase in the number of data sources, and also as a result of a wide array of transitions and disruptions in the m2m platform, we are seeing massive volumes of data being carefully collected, cleansed and stocked in large-scale storage appliances and networks. Such network infrastructures, in synchronization with wide area networking optimization technologies, are designed to efficiently transfer data originating from multiple locations.
Other associated aspects, such as data formats (representation, persistence and exchange), data transmission protocols, data virtualization and data visualization platforms, are systematically accelerating the process by which we extract sense from data heaps. However, the extremely large data volume involved poses the real challenge—and data variety and velocity also inhibit knowledge discovery and dissemination. The open-source community and telecom m2m platform vendors across the globe have long pondered the strategic significance of methodical data analytics for sustainably enhancing business efficiency and value. As might be expected, they offer some solid advancements in their analytical solutions and services that intelligently streamline next-generation data analytics to arrive at diagnostic, predictive, prognostic and prescriptive insights.
IoT data analytics helps make sense out of IoT data, empowering all sorts of connected, embedded (physical, mechanical, electrical and electronic) devices, appliances, instruments, equipment, wares, utensils and machines to be intelligent in their operations and m2m solutions. But IoT data analytics also provides great benefits to users and knowledge workers in their day-to-day activities.