How are Big Data and IoT Interrelated?

There has been rapid growth in the Internet of Things (IoT) and big data technologies amongst organizations and individuals. According to Forbes, it’s predicted that the amount of data generated will increase to 175 zettabytes by 2025. This would have tremendous implications in the way data is collected, analyzed, and reported.

Considering the amount of data being amassed from IoT sensors every second, it’s imperative to have advanced analytics systems in place to collect and harness data effectively. These systems should be capable of finding correlations and revealing trends so that enterprises can gauge actionable insights, which can then be used to improve business capabilities.

Since IoT devices collect vast amounts of structured and unstructured data from its sensors, there will be challenges associated with processing and depicting them in real-time. This is where the role of big data becomes pronounced. According to Gartner, the three main aspects of big data analytics are the volume, velocity, and variety of data. The potential of big data to process a tremendous amount of information is one of its main advantages. The relationship between big data and IoT is one of symbiosis, where seamless IoT connectivity and consequent big data capture and analysis can help enterprises in having a higher level of understanding to further their prospects.

IoT sensors continuously receive data from a large number of connected heterogeneous devices. With the increase in the number of connected devices, IoT systems need to be scalable to accommodate the inflow of data. Analytical systems handle this data and provide valuable reports that will give enterprises a competitive edge. Since the data is mined based on its type, it is mandatory to bifurcate the data to get the most out of it. There are different kinds of analytics done, depending on the type of data in question. Few of the more common ones are:

Streaming Analytics combines unsorted streaming data from sensors and stored data from researches to find familiar patterns.