IoT 3 mins read

IoT: The New Frontier for Edge Analytics

Edge analytics is not a fad; it allows for a shift from a proactive approach to IIoT to a truly predictive data collection and analysis model.

Sean Riley Sean Riley

Today, on IoT Day, the Internet of Things is at an inflection point; data being collected from devices and sensors is vast, so massive that – a lot of the time – it is not used.

Why? Because the data is not understood! Part of the problem with IoT data and, more specifically, Industrial Internet of Things data is that it is collected on central servers. Most of the time, these servers are located either in the cloud or part of an in-house data system. For a manufacturer in need of real-time IIoT data analysis of a device or sensor, this creates latency and added complexity.

The solution is bringing analytics to the edge, allowing data to be analyzed at the point where the equipment is actually transmitting the information. That way there is no networked cloud or server data to sift through.  Latency is reduced and security is boosted.

IoT edge analytics is perfect for manufacturers who need to be able to analyze and take the corresponding action in response to the massive amounts of data transmitted by IIoT sensors or the data transmitted from the production line and have a very short time tolerance.

Edge analytics cuts reaction times and increases data security, especially in production facilities which create a continuous stream of data ripe for attacks.

There are a few more benefits for manufacturers who implement edge analytics processes into their IIoT programs.

Varied connectivity and data mobility

Implementing edge technologies removes the potential downtime risks and connectivity issues often inherent in production lines and manufacturing centers. Edge analytics systems can operate in places that might limit or require intermittent connectivity to the cloud.

Real-time decision making

Edge analytics allows data to be processed instantly, at least in sub-second speeds.  For manufacturers creating advanced electronics, specialized components or specialized robotics, to name a few examples, have very brief time frames where issues can be identified and data can be analyzed to actually capture value. IIoT devices and sensors need to be able to do analytics locally without first sending data to the cloud, so decisions can be made rapidly.

Localized compute power

Many IIoT sensors and devices have space constraints due to the nature of manufacturing. This creates an environment in which fast, secure, and confident decisions can be made at the device level without the support of bigger computing power – ensuring reliability and uptime performance.

New storage and security needs

As the numbers of sensors and IIoT manufacturing devices generating data on remote and, sometimes mobile, devices grow, so does the need for not just efficient storage but data that can be secured in a variety of environments. Limiting the transfer of data to one step versus moving to servers or the cloud eliminates an easily exploitable threat.

Building edge analytics into manufacturing is similar to starting an IoT system; you need to start with a simple threshold alerting systems that are easily and quickly understood by production engineers, product managers, or field service technicians.

Edge analytics is not a fad; it allows for a shift from a proactive approach to IIoT to a truly predictive data collection and analysis model.

This article first appeared on You can read this article in its entirety here.

Happy IoT Day!