IoT data analytics describes the methods by which organizations gather large volumes of information (which can reach the petabyte level for some businesses) and analyze it go gain a better understanding of their own operations and their clients. Optimizing this process takes place in four stages, starting with the collection of data (generally unstructured) generated by IoT devices. This information is then stored in an appropriate big data IoT analytics system, which is much like an extended database. The system carries out IoT analytics processes (which may be tailored to meet the needs of an individual organization), and then generates reports in a user-friendly manner.
Critically, IoT data analytics must happen in real-time, or as close to it as possible. The reasons for this are clear when one considers predictive, proactive maintenance and troubleshooting and the cost-savings they can generate compared to a reactive approach. Predictive and preventive maintenance are deployed widely in the telecommunications industry, but are being adopted increasingly by many other sectors and companies whose operations include field service for all manner of devices.