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.

Learn more at: https://searchbusinessanalytics.techtarget.com/definition/edge-analytics?src=6796305&asrc=EM_ERU_140825236&utm_medium=EM&utm_source=ERU&utm_campaign=20201124_ERU%20Transmission%20for%2011/24/2020%20(UserUniverse:%20334604)&utm_content=eru-rd2-rcpE


By: Margaret Rouse and Ed Burns, and Brien M. Posey