Making the Most of Edge Analytics

By IoTEvolutionWorld Special Guest
Jim McHugh, VP, UCS and data center solutions marketing, Cisco
July 16, 2015

The emerging Internet of Everything (IoE) is connecting a new wave of devices – from industrial sensors and wearable devices to retail cameras – to the Internet. The data from these devices can reduce costs, increase revenue and improve customer service, but only if that data can be analyzed and acted on quickly.


Achieving this speed will often require analyzing this data at the “edge” of the organization. While this historically referred only to branch offices, the IoE extends that definition to processing resources right next to (or even on) the devices on the IoE. Such edge analytics will allow organizations (or even the devices themselves) to act on new insights within milliseconds rather than waiting for the data to be transmitted to a central data center for processing.

The speed and agility benefits are so great that most businesses believe that by 2018, 40% of IoT-created data will be stored, processed, analyzed, and acted upon close to, or at the edge, of the network rather than in centralized data centers, according to IDC Futurescape: Worldwide IoT 2015 Predictions.

Analytics at the Edge

The insights from these devices can be applied not only in marketing and manufacturing, but to human resources, legal, sales, product management, finance, customer support, and more. Consider these examples:

  • Historically, offshore oil rigs have transmitted data such as the status of drill bits through satellite or even physical media to data centers for analysis, resulting in delay before the results can be relayed back to the rig. Edge analytics allows well operators to identify problems in a drill bit, even one operating several hundred feet below sea-level, more quickly and take corrective action before a failure damages the bit or the well.
  • Adding analytic capabilities to security cameras allow real-time identification of unusual behavior, such as a group of people gathered by an entrance in the middle of the night. Rather than waiting to send that data to the cloud for analysis, the camera could identify the potential threat on-site and trigger an alarm more quickly.
  • Location-based services such as identifying open spaces in parking garages for smartphone users can use local servers to process data in real time, providing more accurate results than centralized analysis while reducing data transmission costs.

It’s important to note that edge analytics will exist in addition to, but won’t replace, traditional “Big Data” analytics done in the data center. Central IT will still process the majority of large, longer-lived data sets for analysis of historical trends for purposes such as price optimization and predictive analytics. In the case of the security camera, the edge analytics triggering the immediate alarm could later be combined with camera data from multiple factories to identify long-term security trends.

Technology Requirements:
Delivering analytics to the edge of the network requires new data flows, processing requirements and network management capabilities. For example:

  • Enterprises implementing edge analytics will need routers that provide virtualized computing, network and storage resources to remote or branch locations, as well as ruggedized servers for harsh environments.
  • Hosting providers that support these enterprises will need end-to-end visibility into traffic patterns and network performance to provide and maintain the right infrastructure to support edge analytics. For providers that also supply entertainment and other services to retail customers, such data will help them offer more accurate recommendations on the types of entertainment a customer may enjoy or provide usage alerts before the end of billing cycles to reduce customer costs.
  • Internal IT departments may need their own access to such network and usage data to align capabilities such as data management and data governance with their business objectives. These insights could, for example, help them to identify needs for collaboration technology in branch offices or to identify and better fight fast-changing security threats.
  • Specific verticals such as retail may need technical and business consulting to, for example, correlate in-store video feeds and Wi-Fi data with inventory data to determine where shoppers are spending more time and what shelves need restocking.  
  • Event organizers might need specialized tools and expertise to combine sales data with device usage data to track guest location and behavior. This can help them to meet changing needs, such as providing extra staffing at concession stands or extra security at potential trouble spots.

The possible uses of edge analytics are limited only by your imagination. We’re still early on in implementing this new analytic model, but now is the time to begin evaluating the business cases and what it will take to implement them. 

Edited by Ken Briodagh
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