Edge Analytics – An Easy Guide For 2021
INTRODUCTION
Putting it simply, Edge Analytics is the collection, processing and analyzing of data to a sensor, a network switch or any other connected device. Edge Analytics is data analytics in real-time and on site where data collection is taking place. It could be diagnostic, descriptive or predictive analysis.
According to Gartner, edge analytics will enable users to leverage data analytics ‘beyond those of traditional business insights’ and will increase the efficiency by spotlighting the smallest detail with exceptional precision of analysis and relevancy.
1) DEFINITION
Edge analytics is a model or an approach to data collection and analysis in which, instead of waiting for the data to be sent back to the centralized data store, an automated analytical computation is performed on data at a sensor, network switch or any other device.
WHY USE EDGE ANALYTICS- Edge analytics is just not another gimmicky term invented to make our lives more difficult. It is highly popular because of the few key benefits it provides. They are as follows:
- The first and the most important point is to reduce the latency of data analytics. In many environments, there may not be sufficient time to send data to the central data store and wait for their decisions at the time of emergencies. It may be more efficient to analyze faulty data right there and shutoff the valve immediately if required.
- The scalability of analytics is the second benefit that it provides. It helps decentralizing to the sites where the data is actually collected.
- Low bandwidth environmental problems can also be dealt with edge analytics. It provides analytic capabilities in remote locations.
- And lastly, it will reduce overall expenses by minimizing bandwidth, scalability of analytics and reducing the latency of data analytics.
2) APPLICATION OF EDGE ANALYTICS
The excessive adoption of internet of things (IoT) universally resulted the significant increase on edge analytics application.Edge analytics prove to be very useful in a number of industries and sectors. Let’s start with ana example. Suppose, a device that controls the temperature of a refrigerator could detect a change which can be quite dangerous and can damage the products in seconds, in the internal temperature of the refrigerator. Here, delay couldn’t be afforded.
But if the data were required to go back to the central server, be processed and parsed and then relayed back to the sensor, the goods inside the fridge would undoubtedly spoil. But with Edge analytics, this problem could be solved in a jiffy with the help of the sensor which relays the problem and implements a solution instantly.
CONCLUSION
Despite the few possible challenges of implementation, the edge analytics model does not fail to present a long-lasting trend which enables the users to obtain valuable insights in real-time, bring structure to unstructured content and feed relevant data to the cognitive-oriented systems.
The market players are investing in storage and networking gear at the edge for a perfect facilitation of work process and strategic exploration of advanced analytic capabilities without fails. It helps them to make the most of the collected data.
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