Recently, IDC said that by next year 40% of the data we access will be stored, processed, analyzed, and acted upon, close to, or at the edge of a network. As I see it, this is due in part to the greater acceptance of the Internet of Things along with the availability of more computer processing power that is now available at lower cost, which in turn is enabling us to expand our reach to a range of devices that gather, analyze and react to data in a variety of applications. This combination has allowed us to move from just “connected devices” to connected devices that redistribute and process data and analytics independently at the Edge.
The Edge is contributing to a significant shift in the way we are acquiring information, interacting with it, and making decisions. It is enabling us to expand our reach to a new range of equipment and devices to deliver relevant outcomes.
What is the Edge?
Simply put, it is a means to collect and process data at the device level rather than in the cloud, at the enterprise or remote data center. It represents machine or device-level execution of application components traditionally associated with enterprise applications, at a place where we can process and analyze data as close to the original sources as possible.
Why is it important?
Some of the data analysis needs to be done at the device and equipment level on a network. The Edge is where these assets are optimized and the data to be gathered and utilized reside. Pushing all the data from all these assets into the cloud or elsewhere is too costly and time-consuming.
When it comes to devices and equipment that can reside on the edge, there are several. Equipment such as RTUs, chillers, plant level controllers, meters, sub-meters, sensors and HMIs, security cameras, gateways, routers, wireless access points, field devices and lighting are good examples. Edge computing is well suited for many machine-to-machine and IoT applications.
What is driving the move to the Edge?
There are several factors including:
- Breadth of connectivity options that are available today
- Advent of new software and applications (as devices become more intelligent, software and apps are playing a bigger role)
- Real-time data requirements
- Power and “smartness” of hardware (more powerful, more capacity, higher levels of data processing, increased storage capabilities)
- Open systems, open source, open programming, open hardware technologies
- Adoption of the network Edge perspective in the context of the IoT
- Flattening of the traditional architecture
- Reduces data overload
- Reduces the amount of data that needs to be exchanged with the Cloud (putting data into the Cloud is costly and time-consuming)
When it comes to value, the Edge is delivering on many value points including access to data in real-time and for many implementations. Sending data to the Cloud or a remote data center is slower than processing it at the Edge. Processing data at the Edge also enables network bandwidth conservation, thus reducing operational costs and overall data management. With less data being sent elsewhere, it helps keep data from causing issues within the networks. Processing data at the Edge plays a valuable role in providing efficiency, security, and compliance.
With lower costs and enough computing power, we can expect more data activity to move to the Edge. The end goal is to ensure that the data generated and received from devices is the best you can get, and any conclusions derived from the data can be followed and acted upon within the timeframe needed and required to deliver the maximum value.