Edge Analytics

This process involves collecting, analyzing and making decisions based on data that was generated within the same physical environment. The cloud isn’t involved in the process of data analysis, and all of the work is done on embedded devices.

Benefits of Edge Analytics

Edge analytics has received more attention with the growth of the Internet of Things. For many businesses, streaming data from disparate IoT sources creates a huge store of data which is difficult to manage. By filtering the data through an analytics algorithm as it’s created at the edge of the network, parameters can be set to decide what data is worth migrating to the cloud or data store.

Analyzing data as it’s generated decreases latency in the decision-making process as well. For example, if an individual component of a system suffers a failure, the algorithm interprets that data and automatically shuts it down. This may save a lot of time in transporting data to a centralized store, in addition to reducing or avoiding equipment downtime.

Edge analytics also provides scalability. Analytics algorithms applied to sensors and devices relieve the strain on management and analytics systems, regardless of the number of connected devices and the size of the network. This allows the system to scale quickly and easily, no matter how much the data grows.

Security is less of a concern with edge analytics as well, compared to centralized cloud providers. Data breaches are common with centralized clouds, since data can be intercepted through a hack or from lack of proper security controls. With edge analytics, data stays within your own firewall, limiting the risk of a breach.

Limitations of Edge Analytics

Edge analytics is a relatively new technology, so not all hardware is capable of storing data or performing complex processing. That said, it’s likely that this will change in the coming years.

Businesses should also consider whether or not it makes sense to invest in edge analytics, since it’s best suited for scenarios that need to optimize for speed, security or efficiency. As with any new architecture, there remain some engineering obstacles to successfully deploying an edge analytics application.