Experts Cautious About 5G’s Impact on IoT Data Architectures

Taking It Case by Case

Regardless, Doffmann said, “There will be far more thought given to where you put your AI, which is of major value in IoT.”

That determination will not be focused solely on cost. “The issue about where you process the analytics or where you process the information is much more around two areas. First, it’s a latency issue. How quickly do you need the sensor piece to work? Second, how much data is actually needed back at the center for broader analytics?,” Bevans said. 

He continued: “When talking about longer scale projections, a lot of what we see is that a local edge-based environment needs the analytics and needs sensors to respond, and then you go to the cloud if you need a level of machine learning to get better at the automation. It’s a data architecture that will drive how much goes backwards and forwards, and what a business needs in terms of latency.”

The Bottom Line 

All that said, questions of cost still arise. “Large operators have started hinting at tiered pricing based on bandwidth, latency and SLAs in terms of service time,” Joshipura said. “That would mean additional revenue for telecos, and from an enterprise perspective, it would move some workloads and apps from a centralized cloud to a distributed cloud.” 

The result? “For enterprises, the cost might be a wash, but the services and the experience will improve for the same application. As enterprises come up with more applications, it will cost them more money,” Joshipura predicted. “There will be a top-line increase from an end user perspective, while distributing OPEX and CAPEX below it.”