Their VP and GM of data and analytics, Mike Flannagan, told me “For me the idea of analytics at the edge is about the notion of doing the right amount of processing of data at the right place.
“If you’re generating tons of data in your data center, then analyze it in your data center.
“If you’re generating tons of data at the edge, and you have unlimited bandwidth, then you can still send it all back to the data center – its fine.
“But there are environments where that is not possible, and the two main things that come together to make it not possible are the volume of data and the perishable nature of data.”
It is worth noting that Flannagan does not in any way see edge-based analytics replacing the centralized data center model. Rather it is an approach which can be used to supplement or augment analytics capabilities in certain situations, such as when insight needs to be acted upon very quickly.
An interesting example comes from the world of powerboat racing. The 200mph twin engine boats form part of a data system which includes a land crew constantly receiving and analyzing data on the boats’ performance as they race, often many miles offshore.
However one particular algorithm generates data which needs to be fed back to the pilot within a split second. The powerful engines must operate at the highest output levels possible without burning out, to stand a chance at breaking world records and winning races. SilverHook Powerboats implemented a system which gives the pilots instant feedback, allowing them to ease off on the throttle at the right point, based on real-time analytics carried out within the boats. This potentially shaves seconds off the time it would take if the data was sent to the remote team first, for processing, and prevents expensive, race-losing engine failure.
Large retailers could analyze point of sales data as it is captured, and enable cross selling or up-selling on-the-fly, while reducing bandwidth overheads of sending all sales data to a centralized analytics server in real time.
Emergency repair work and equipment down-time can be reduced when manufacturers build edge-based analytical systems into machinery and vehicles, allowing them to decide for themselves when it is time to reduce power output or send an alert that a part may be due for replacement.
Smart City architects can build edge analytics into systems such as traffic signals, allowing intelligent monitoring and management of traffic. Pollution levels caused by traffic could be monitored in real time and regulated by reducing traffic flow when air quality falls below a certain level.
Autonomous and driverless vehicles will heavily rely on edge analytics systems for functions that require immediate response, such as hazard avoidance. At the same time they will rely on centralized analytics for fleet management and optimization of pathfinding. They will also rely on a middle ground, sometimes known as “the fog”. This involving analytics carried out between a network of vehicles which are close together, for the purpose of managing local traffic flow.
Of course edge analytics is not suitable for every IOT implementation. Flannagan says “Clickstream analytics – information about web traffic – all that data is centralized so there is no edge component. It would be ridiculous to suggest that any sort of distributed analytics would be necessary.
“What I am really evangelizing is the need to carefully plan where you process data to make sure you’re processing it at the most efficient place, otherwise you are unnecessarily spending money to move data around, that you don’t have to spend.
This article was originally published on www.forbes.com and can be viewed in full