There has been rapid growth in the Internet of Things (IoT) and big data technologies amongst organizations and individuals. According to Forbes, it has been 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 is imperative to have advanced analytics systems in place to collect and harness the data effectively. These systems should have the capabilities to find correlations, and reveal trends so that enterprises can gauge actionable insights from it, 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 this 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.
Analyzing Big Data
IoT sensors constantly 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 provide enterprises with a competitive edge. Since the data is mined based on its type, it 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. The real-time analyses from this method can help in use cases like fleet tracking and bank transactions.
Geospatial Analytics: Another kind of big data analytics method is the geospatial kind, where a combination of IoT sensor data and the physical location of sensors can provide a holistic perspective for predictive analysis. The sheer number of objects in the connected IoT world and its ability to send data via wireless networks help in obtaining detailed data dumps which that can be used to facilitate insights.
We are currently at a stage where capturing, analyzing, and reporting of IoT data is mandatory for most businesses. However, there are quite a few challenges faced by these organizations since these technologies are still in their developing stage. Some of them are:
Integration: Since IoT data is received in different formats over multiple channels, collecting and integrating it is challenging. Analytics systems need to ensure that the data received is in a format that is actionable enough to determine insights. Text mining and machine learning technologies are often used to extract textual data from sensors. However, extracting non-textual formats of data like images, videos cannot be done easily.
Privacy: IoT systems often have information that is sensitive in nature and needs to be protected from outside interference. The constant influx of data makes it difficult to secure each part of it and to perform analysis, and these systems rely on third-party infrastructure due to their limited capacity, which will give rise to increased security risks. Therefore preventive measures like data anonymity and encryption are employed to enforce data security.
IoT is one of the most innovative developments in the recent decade that successfully blends technology and data to develop more constructive strategies. With the proliferation of sensors and smart devices in the last decade, the relationship between IoT and big data has reached a stage where accurately processing massive amounts of high-frequency data is of paramount importance to organizations. To go along, an integration platform that can assimilate, analyze, and derive insights for businesses is the need of the hour and the right strategy in this interconnected world.
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