The significant growth in data being produced around the world has led to an increased demand from companies trying to use it as quickly as possible in order to influence decision making, and improve their business.
The problem with accessing data quickly is that much of it is being created at the edge, or from remote devices, typically this would mean that data would need to be sent back to a centralised data store to be processed, which takes time.
To combat this edge analytics proposes to collect and analyse by means of analytical computations on data at a sensor, network switch, or other device. This means that the data no longer has to be sent back to a central store before it can be processed.
Edge analytics has increased in popularity as the Internet of Things model of connected devices has gain traction.
Several benefits arise from this, faster time to information, less stress on the network, lower costs, and scalability.
By pushing analytics to sensors and network devices the strain on enterprise data management and analytics systems is reduced.
Edge analytics has increased in popularity as the Internet of Things model of connected devices has gain traction.
Another benefit of running edge analytics is that an analytic algorithm can assess the data as it is created and make decisions as to what information is worth storing, either in a cloud or on-premises data centre. This means that businesses should be able to better manage what data they are storing, making sure that what they keep hold of is of value and is not redundant, trivial, or obsolete (ROT) data.
Several vendors are working on edge analytics, also known as edge computing. Included in the ranks of those working on solutions in this area are the likes of HPE, Cisco, Juniper, and ETSI.
Industries likely to be at the forefront of using edge analytics are thought to be manufacturing, telecoms, the oil and gas industry and others.
