An unprecedented amount of data has been produced by the widespread usage of Internet of Things (IoT) devices; this data needs to be acquired, stored, and analyzed using sophisticated tools. In order to manage and interpret the massive amounts of data produced by IoT devices, new methods are therefore becoming increasingly necessary.
Edge computing for data collection and archival is one strategy that is conceivable. Edge computing enables data to be processed nearer to the point of origin, decreasing latency and enhancing performance. A less quantity of data must be transported to the cloud thanks to edge devices’ ability to filter and aggregate data.
The use of machine learning algorithms for data analytics is a viable alternative. Large amounts of data can be mined for insights using machine learning, which can also be used to spot trends and generate predictions. Moreover, machine learning models can be installed on edge devices to facilitate real-time decision-making and analytics.
Ultimately, businesses and organizations have a huge opportunity to improve operations, cut costs, and improve customer experiences through the integration of IoT devices and data analytics. Data security and privacy, interoperability, and scalability are just a few of the issues that still need to be resolved.