The traditional approach to analytics is not the right fit for the Internet of Things. The new approach is Edge Analytics. What is it? It is an approach to data collection and analysis in which an automated analytical computation is performed on data at a sensor, machine, gateways or other device instead of waiting for the data to be sent back to a centralized data store.
In other words, it is a multitude of devices or sensors that are scattered across any network or embedded throughout a product (train, jet engine, security camera, smart phone) that is generating data about the operations and performance of that specific device or sensor.
Edge analytics has gained attention as the Internet of Things (IoT) model of connected devices has become more prevalent. Enterprises must harness the smartness of the myriad of smart devices and their low cost computational power to allow them to run valuable analytics on the device itself.
By running the data through an analytics algorithm as it’s created, at the edge of a corporate network, companies can set parameters to define the information worth sending to a cloud or on-premises data store for later use and what information is processed locally.
Edge Analytics allows a hierarchical distributed IoT Analytics schema. How does it work?
- Simple analytics on the smart device itself
- More complex Multi-device analytics on the IoT gateways
- Big data analytics running in the cloud
This distribution of analytics offloads the network and the data centers by creating a scalable model. Distributing the analytics and intelligence is inevitable and desirable. It will help companies in dealing with big data and releasing bottlenecks in networks and data centers.
Analyzing data as it is generated can also decrease latency in the decision-making process on connected devices. Edge IoT analytics is about operational efficiencies and scalability. What does it mean? Many business processes do not require heavy duty analytics and therefore the data collected, processed and analyzed on or near the edge can drive automated decisions.
This is what Harriet Green IBM VP and GM of IoT has to say about Edge Analytics: “It’s about analyzing time-critical information on site, in real time. And it’s about filtering out other data, moving it to the cloud, integrating it with important contextual information, and using it to inform strategic decision-making and long-term investments. Put simply, it’s about maximizing the time to benefit of all IoT data.”
In summary, edge analytics will help organizations to eliminate the time, bandwidth and expense required to transport the data, and make possible to take immediate action in response to specific parameters defined by them. This new way to manage analytics speeds up and simplifies the process in a way that’s never been done before.
The research company IDC recently predicted in his IDC FutureScape for IoT report the following: “by 2018, 40% of IoT data will be stored, processed, analyzed and acted upon at the edge of the network where it is created”.
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