How Edge Analytics Reduces Cloud Load And Why Is It Essential?

Thanks to the surge of IoT devices and a connected world powered by 4G and 5G, brands are exploring new ways to gather data in new and inventive ways. However, with more IoT devices acting as data touchpoints, new data sources and endpoints are getting adding to the circuit. This implies we also need to process such a magnitude of data effectively and in real-time to ensure the effective functioning of data based services. Besides, these continuous streams of (usually unstructured) data, need to be refined and correlated with a myriad of other connected devices, sensors, and data sources. Although existing cloud analytics and cloud platforms can handle this data hunger, there is a major bottleneck: latency. Edge helps in quick data processing with lower latency connections, all while maintaining central control. Therefore, in edge, data is processed by the IoT connected device by itself or by a local server, rather than being transmitted to a data center or cloud. Further, edge reduces bandwidth while also offering enhanced security and data privacy.

In simpler terms, edge analytics is a data analysis model where incoming data streams are analyzed at a non-central point in the system. This usually occurs at or near a sensor, network switch, peripheral node, or other connected devices. Also, owing to its decentralized and local nature,  edge analytics has a benefit over more traditional big data methods, i.e., it is much faster, leading to quicker, more accurate business intelligence while also lightening the load on the network. It is important to remember that edge computing and edge analytics are not exclusive; rather the latter takes edge computing to the next level by collecting more data at the edge and applying more complex analysis to it. Also, edge analytics market dynamics demonstrate steady growth of edge computing adoption.

Why is it better?

Today, more companies are turning to Edge Analytics, together with the cloud, to better manage IoT data floods. Further, it enables real-time alerts for outages, ensures consistent uptime and provides real-time data streams for machine learning and predictive maintenance initiatives. Edge analytics also aids in autonomous decisions about what and when to transmit information and be economical in reporting it without risking missing crucial information. This is why, most edge analytics software is embedded inside of connected devices and nearby gateways.  These device types are optimized for low power & cost and lack the capability to retain and perform powerful analytics on enormous data volumes. With incoming data flow, the existing historical data at edge are combined to unlock new insights, which saves time and frees cloud resources. This is also why, edge analytics is considered to be the future of sensor data handling.

Furthermore, edge analytics reduces the work on backend servers and delivers analytics capabilities in remote locations switching from raw transmission to metadata. This form of data analytics is poised to augment computer vision and video analytics. Here, edge analytics will implement distributed structured video data processing, and take each moment of recorded data from the camera and performs computations and analysis in real-time. So, basically, it is the lynchpin that drives real-time decision-making from audio-visual sources too. Moreover, it will also play a crucial role in developing self-driving cars, AI voice assistants, connected traffic systems, and industries like oil and fuel, healthcare and more.

Key Challenges

While edge analytics can be viewed as opposite to traditional big data analytics, which is performed in centralized ways, it is not here to replace cloud analytics. However, there are a few concerns that need to be addressed first. Some edge analytics systems share only their output with the cloud due to bandwidth or storage constraints. This would restrict businesses from reviewing the raw inputs that led to the analyses shared with the cloud systems. Next, at present not all IoT devices and edge devices can store their data or perform complex processing and analytics. Lastly, there is currently no regulatory framework for edge devices either.