Media & Marketing


Carlos Barge

Edge Analytics is an approach to data collection and analysis in which an automated analytical computation is performed on data at a sensor, network switch or other devices instead of waiting for the data to be sent back to a centralized data store.

Edge analytics has gained attention as the Internet of Things (IoT) model of connected devices has become more prevalent. In many organizations, streaming data from manufacturing machines, industrial equipment, pipelines and other remote devices connected to the IoT creates a massive glut of operational data, which can be difficult – and expensive – to manage. By running the data through an analytics algorithm as it’s created, at the edge of a corporate network, companies can set parameters on what information is worth sending to a cloud or on-premises data store for later use — and what isn’t.

Analyzing data as it’s generated can also decrease latency in the decision-making process on connected devices. For example, if sensor data from a manufacturing system points to the likely failure of a specific part, business rules built into the analytics algorithm interpreting the data at the network edge can automatically shut down the machine and send an alert to plant managers so the part can be replaced. That can save time compared with transmitting the data to a central location for processing and analysis, potentially enabling organizations to reduce or avoid unplanned equipment downtime.

Another primary benefit of edge analytics is scalability. Pushing analytics algorithms to sensors and network devices alleviates the processing strain on enterprise data management and analytics systems, even as the number of connected devices being deployed by organizations – and the amount of data being generated and collected – increases.

The concept of edge analytics brings with it the possibility of designing an optimal model that provides the opportunity of managing the data transfer from the edge and data storage at data centers in an efficient way.

How to use Edge Analytics

While edge analytics is still a specialized tool, they are useful in a variety of industries and sectors. One of the most common uses is in IoT edge analytics, which allows for network controllers to have a much better real-time picture of how devices and sensors are operating. In this case, devices transmit data back to a central location, but the bulk of analysis happens on-site.

For instance, a device that controls the temperature of a refrigerator at a supermarket could detect a dangerous change in internal temperature that could cause damage to products in seconds. If that data were required to travel back to a central server, be processed and parsed, and then be relayed back to the sensor, the refrigerator’s goods could quickly spoil.

With edge analytics, the same problem could be resolved in a few seconds with the sensor instantly relaying the problem and implementing a solution.

Similarly, manufacturing plants can use edge analytics to keep better track of machinery health, production output, and be ready to deal with any crisis that arises in seconds instead of minutes.

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