edge analytics
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 device 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 to 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.
How is edge analytics used?
One of the most common use cases for edge analytics is monitoring edge devices. This is particularly true for IoT devices. A data analytics platform might be deployed for the purpose of monitoring a large collection of devices for the purpose of making sure that the devices are functioning normally. If a problem does occur, an edge analytics platform might be able to take corrective action automatically. If automatic remediation isn't possible, then the platform might instead provide the IT staff with actionable insights that will help them to fix the problem.
Benefits of edge analytics
Edge analytics delivers several compelling benefits:
- Near real-time analysis of data. Because analysis is performed near the data -- often on board the device itself -- the data can be analyzed in near real time. This would simply not be the case if the device had to transmit the data to a back-end server in the cloud or in a remote data center for processing.
- Scalability. Edge analytics is by its very nature scalable. Because each device analyzes its own data, the computational workload is distributed across devices.
- Possible reduction of costs. Significant costs are associated with traditional big data analytics. Regardless of whether the data is processed in a public cloud or in an organization's own data center, there are costs tied to data storage, data processing and bandwidth consumption. Some of the edge analytics platforms for IoT devices use the IoT device's hardware to perform the data analytics, thereby eliminating the need for back-end processing.
- Improved security. If data is analyzed on board the device that created it, then it's not necessary to transmit the full data set across the wire. This can help improve security because the raw data never leaves the device that created it.
Limitations of edge analytics
Like any other technology, edge analytics has its limits. Those limitations include:
- Not all hardware supports it. Simply put, not every IoT device has the memory, CPU and storage hardware required to perform deep analytics onboard the device.
- You might have to develop your own edge analytics platform. Edge analytics is still a relatively new technology. Although off-the-shelf analytical platforms do exist, it's entirely possible that an organization might have to develop its own edge analytics platform based on the devices that it wants to analyze.
Applications of edge analytics
Edge analytics tend to be most useful in industrial environments that use many IoT sensors. In such environments, edge analytics can deliver benefits such as:
- Improved up time. If an edge analytics platform can monitor a sensor array, it might be able to take corrective action when problems occur. Even if the resolution isn't automated, simply alerting an operator to a problem can help improve the overall up time.
- Lower maintenance costs. By performing in-depth analysis of IoT devices, it might be possible to gain deep insight into device health and longevity. Depending on the environment, this might help the organization to reduce its maintenance costs by performing maintenance when it's necessary rather than blindly following a maintenance schedule.
- Predict failures. An in-depth analysis of IoT hardware might make it possible to accurately predict hardware failures in advance. This can enable organizations to take proactive steps to head off a failure.
Edge analytics vs. edge computing
Edge computing is based on the idea that data collection and data processing can be performed near the location where the data is either being created or consumed. Edge analytics uses these same devices and the data that they have already produced. An analytics model performs a deeper analysis of the data than what was initially performed. These analytics capabilities enable the creation of actionable insights, often directly on the device.
Cloud analytics vs. edge analytics
Both cloud analytics and edge analytics are techniques for gathering relevant data and then using that data to perform data analysis. The key difference between the two is that cloud analytics requires raw data to be transmitted to the cloud for analysis.
Although cloud analytics has its place, edge analytics has two main advantages. First, edge analytics incurs far lower latency than cloud analytics because data is analyzed on site -- often within the device itself, in real time, as the data is created. The second advantage is that edge analytics doesn't require network connectivity to the cloud. This means that edge analytics can be used in bandwidth-constrained environments, or in locations where cloud connectivity simply isn't available.
