How does edge computing impact decision support?

by Daniel J. Power
Editor, DSSResources.COM

Edge computing means more data for predictive analytics and model-driven decision support can be processed quickly at the point of origin. Edge refers to where computing and analytics occurs. Internet of Things (IoT) devices are at the "edge" of networks. These edge devices can often process data in real-time as it is collected rather than streaming the data to the Cloud for processing. In a distributed computing environment, the devices collect data, communicate with other devices and increasing process and summarize the data.

Edge computing and IoT are integral parts of the Fourth Industrial Revolution (4IR) and Industry 4.0. This complex distributed computing environment is "blurring the lines" between the physical, digital, and even biological domains or spheres.

It is possible to run Artificial Intelligence (AI) applications and machine learning with analytics at the edge. The performance of the tasks depends upon the size and scale of the edge site and the particular system being used.

While edge site computing systems are much smaller than central data center systems, distributed edge sites have matured and run many varied workloads and applications successfully.

Many large retailers are using edge computing because of the high cost to send data to the cloud for processing. Also, often a cloud architecture is not able to meet real-time demands. Retailers are running analytics applications and AI algorithms at edge sites.

Edge computing helps scale decision support applications.

Last update: 2019-02-10 12:11
Author: Daniel Power

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