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IoT analytics (Internet of Things analytics)

Ivy Wigmore
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IoT analytics is the application of data analysis tools and procedures to realize value from the huge volumes of data generated by connected Internet of Things devices.  

The potential of IoT analytics is often discussed in relation to the Industrial IoT. The IIoT makes it possible for organizations to collect and analyze data from sensors on manufacturing equipment, pipelines, weather stations, smart meters, delivery trucks and other types of machinery. IoT analytics offers similar benefits for the management of data centers and other facilities, as well as retail and healthcare applications.

IoT data can be thought of as a subset and a special case of big data and, as such, consists of heterogenous streams that must be combined and transformed to yield consistent, comprehensive, current and correct information for business reporting and analysis. Data integration is complex for IoT data. There are many types of devices, most of which are not designed for compatibility with other systems. Data integration and the analytics that rely on it are two of the biggest challenges to IoT development. 

Big data is sometimes characterized by the 3Vs model: Volume, variety and velocity. Volume refers to the amount of data, variety refers to the number of different types of data and devices, and velocity refers to the speed of data processing. The challenges of big data analytics – and IoT analytics -- result from the simultaneous expansion of all three properties, rather than just the volume alone. 

This was last updated in July 2017

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