Internal sensors collect data from IoT consumer devices, such as security systems, smart appliances, smart TVs, and wearable health meters. Data are collected from commercial devices, as well, including commercial security systems, traffic monitoring devices, and weather tracking systems. The data are transmitted, saved, and can be retrieved at any time.

IoT analytics is performed by applying data analysis tools or procedures to the various types of data IoT devices generate. Using IoT analytics, valuable information can be extracted from massive data collections that can then be used to improve on procedures, applications, business processes, and production. Several types of data analytics can be used on IoT data:

Prescriptive analytics. Prescriptive analytics is used to analyze which steps to take for a specific situation. It’s often described as being a combination of descriptive and predictive analysis. When used in commercial applications, prescriptive analytics helps decipher large amounts of information to obtain more precise conclusions.

Spatial analytics. This method is used to analyze location-based IoT data and applications. Spatial analytics deciphers various geographic patterns, determining any type of spatial relationship between various physical objects. Parking applications, smart cars, and crop planning are all examples of applications that benefit from spatial analytics.

Streaming analytics. Streaming analytics, sometimes referred to as event stream processing, facilitates the analysis of massive “in-motion” data sets. These real-time data streams can be analyzed to detect emergency or urgent situations, facilitating an immediate response. The types of IoT data that benefit from streaming analytics include those used in traffic analysis, air trafficking, and the tracking of financial transactions.