Revamping Retail Strategies with Edge Computing

Edge computing moves data processing and analytics closer to the points where the data is born. As loosely defined as it may sound, the edge exists wherever the digital world and physical world intersect, and data is securely generated, collected, and processed to create new value

FREMONT, CA: Day to day operations in the retail sector today generate massive amounts of data. While this can be very exciting for most retailers, it could also become overwhelming if not dealt with the right way. The large amounts of data can help retailers gain intimate insights into their customers, products, and operations. The retailers can then use these data to provide rich customer experiences, optimize business processes, and unlock new monetization opportunities. Retailers with the systems, strategies, and analytics tools in operation or in place can capitalize on the growing amounts of data generated by multichannel customer interactions, Internet of Things devices, computer vision systems, and a wide range of business processes.

Edge computing

Edge computing moves data processing and analytics closer to the points where the data is born. As loosely defined as it may sound, the edge exists wherever the digital world and physical world intersect, and data is securely generated, collected, and processed to create new value. There are three primary reasons for retailers to move data analytics to the edge, rather than sending everything to analytics engines in corporate and cloud data centers.

Volume: Edge computing generates an astronomical amount of data. Estimates suggest that edge computing could account for nearly three-fourths of an organization's data within the next two years. The higher the volumes of data, the greater the challenges of moving it all to a faraway data center.

Cost: The cost advantages of operating on the edge will surely increase once the data volumes grow. In most cases, it will be more cost-efficient to process data at the edge rather than to move the data on to a corporate or cloud data center.

Speed: Transmitting data to distant data centers for analysis introduces a great deal of latency into the data analysis equation. In most cases, organizations cannot accept the inherent latency in sending data over a network for processing and then returning a response, especially in the case of security systems that leverage computer vision to keep an eye on the retail environment.

The new and upcoming digital era builds the case for analyzing more data at the edge, where IoT and computer vision devices are located and where intelligent systems can take immediate actions based on the results of data analytics. This is primarily concerned with bringing the analytics to the data, rather than sending the data to the analytics.

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