Having considered what edge computing is and what its benefits are in previous blogposts, let’s take a closer look at edge analytics. We believe that this is one of the most exciting use cases for edge computing because it can help small and medium businesses address some of today’s most significant business challenges.
Let’s start off with a definition of edge analytics: it’s an approach to collecting data from devices at the edge of a network—for example, sensors or network switches—and analysing that data at the point where it is captured rather than sending it back to a centralised data store. Following the analysis of the data, the system will usually make an automated decision.
Edge analytics is a powerful solution to some of the data collection and analysis challenges we face as smart sensors and other Internet of Things (IoT) devices proliferate in smart offices, factories, warehouses and other connected workplaces. It enables us to make smart and efficient use of the wealth of data these devices generate.
Because the data is analysed at the point it is collected, the analysis can be completed in near-real time for almost instant and automated decision-making. This can be transformative in use cases such as monitoring industrial machinery. If the data and analysis suggests a machine is overheating, it can be shut down within seconds following business rules.
Furthermore, with edge analytics, companies can reduce the costs and performance issues associated with sending large volumes of data to the cloud or their data centre for analysis. Better security is another key advantage. Because the data collection and analysis is conducted on the edge, there is less chance of a breach during transfer to the cloud or the data centre.
Edge analytics in action
One interesting use case for South African companies may revolve around access management. Crime is one of the single biggest challenges small and medium businesses face. It’s expensive to pay for a security control room to monitor live video feeds 24/7 to watch for possible security breaches.
With edge analytics, companies could install artificial intelligence (AI) powered cameras to watch key areas of their premises such as the doors and parking lots. Rather than alerting security every time movement is detected at night, the system could use algorithms to refer only real security threats to the security team.
For example, if the access control system and automatic number plate verification system confirms that it’s an employee in the parking lot at 11 P.M., it may not be necessary to alert security. But if the data suggests that the person is unknown to the company, the security team may be referred to the video feed to check it out.
Companies that are running generators that fire up automatically when there’s load shedding could also benefit from edge technology. An intelligent sensor could send an alert if the generator is running too hot, if the fuel level is low, or if someone is tampering with the unit to steal fuel or components.
Edge analytics won’t replace cloud analytics
"Edge analytics isn’t meant to replace cloud analytics—instead, it’s a way to automate processes and protect assets on the network edge in real time. Edge analytics is a perfect fit for operational applications where latency and speed are important. Cloud analytics remains essential for strategic decision-making. Both will find their place in a modern business."