Cisco sees a data analytics fortune at the edge of the network
If you need to know when holiday shoppers are about to hit the checkout stands or when store shelves need to be stocked, Cisco Systems says a router can tell you.
The company that commercialized routers 30 years ago is now using them to bring big-data analysis to the edges of networks, where some types of data may be priceless for a few seconds but not worth storing or sending to a cloud.
That’s one piece of Cisco Connected Analytics for the Internet of Everything, a set of new and existing capabilities that the company is introducing as a portfolio on Thursday. CEO John Chambers and services chief Edzard Overbeek are set to do the unveiling at an event at Cisco headquarters, a measure of the importance Cisco places on its Internet of Everything (IoE) vision, which it pegs as a US$19 trillion economy-wide opportunity over the next 10 years. Analytics is a $7.3 trillion chunk of that, the company says.
Cisco wants to help enterprises and service providers bring together many types of data from different sources and glean insights to help them run things. The company already goes go customers to study their needs and craft a solution. Now some of the solutions it’s put together many times will also be available as packages that don’t require all the custom, on-site preparation. Initial packages include ones for network and video analytics.
The Internet of Things—Cisco includes related products and services and calls it IoE—may change the landscape of computing and networking by spreading both processes across more sites. In some cases this may be the only way to make IoT feasible, because sending data across networks costs time and money.
While core databases and applications still generate much of the big data in enterprises, another wave of bits is coming from the edges of networks. In some cases, to glean useful information from those data streams, enterprises will have to do it on the spot, Cisco says. That may be either because there’s too much data to send over a wide-area network or because the round trip to the cloud takes too long.
“You actually improve your ability to make decisions quickly by doing the processing closer to where the data’s created,” said Mike Flannagan, vice president and general manager of Cisco’s Data and Analytics Business Group. Cloud and enterprise data centers will still play roles in analytics, such as learning from data collected over time.
Other vendors, including Intel, also have their eyes on computing at the edge, where thousands of tiny computers could be sold in the next few years. Cisco calls the concept “fog computing” and launched a major effort in the area earlier this year.
Because it supplies many of the networks at stores, branch offices and remote sites, Cisco says it’s in a good position to analyze data on site. For example, it is building data analytics capabilities into its ISR (Integrated Services Router) line, a series of systems for remote and branch networking that is available with a wide range of specialized modules. Other, much smaller and weather-hardened routers designed for use on rail cars or pipelines can do some analysis, too. Cisco’s also building analytics into other products, including video security cameras that can decide whether a given piece of footage is interesting enough to save.
For examples of how video analytics can improve operations, Cisco described a deployment at an unnamed warehouse-type retail store. There, Cisco deployed systems that crunched video data locally to help with inventory tracking and staffing, Flannagan said.
The store trained video cameras on shelves to watch product availability more effectively than point-of-sale systems could, according to Cisco. Rather than relying on employees to check each product in and out of the department on the POS terminal, the new mechanism constantly watched the shelves themselves.
“From the video frames, we were able to determine when a shelf was empty and when a shelf had product,” Flannagan said. If the boxes don’t always get placed on the shelf correctly, machine learning can help the system identify the products anyway, according to Cisco. The cameras don’t have to come from Cisco, Flannagan said.
The video analytics prevented errors stemming from things like returns, where an employee might take a returned, unopened item and put it back on the shelf before recording that action on the POS. The store’s labor costs went down because staffers weren’t wasting time restocking shelves that didn’t need it, and revenue rose because not as many shoppers found accidentally empty shelves, according to Cisco.
Also using video, Cisco also used data about where people were in the store to predict when checkout lines would start to get crowded. The system learned to predict when a lot of shoppers were about to head for the checkout. For example, it found that customers tended to go to the frozen-food aisle right before they checked out. Based on that, the store could call more employees to the front of the store when the frozen section got crowded, Flannagan said.