Definition

edge analytics

Contributor(s): Ed Burns

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 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.

This was last updated in April 2016

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How important do you think edge analytics will be to the success of the Internet of Things?
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As I read the brief article, I was reminded of early days of SCADA (systems for control and data acquisition) communicating 100s miles by slow 2400 baud modems. For more efficient communications, only differences were sent, but there was also a "background sweep" over all parameters, to refresh them in case of some kind of error. Otherwise, if a bit was lost, but rarely changed, it could remain in the wrong state "forever". Similarly, IMO, one must be careful not to filter too much at the "edge". Consider all error cases, comms also.
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Seems to me that Edgeanalytics is a repeat of the 80s/90s client/server model which turned out to be rather expensive and was mostly a means to deal with inadequate bandwidth. Not sure that this reason holds true today.
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