IOT sensors create continuous streams of (usually unstructured) data, which must be refined and correlated with a myriad of other connected devices, sensors, and data sources. For IOT data to be consumable by an application, data must first be converted into a structured format, ideally without introducing additional latency to already time-sensitive data. Anyone who has been involved with a project involving IOT devices, knows the challenge of figuring out how to get, use, and understand the floods of data.
As IOT adoption continues to gain steam, the flood of IOT data will become increasingly difficult to manage. Complicating matters, Gartner has found that “due to a lack of information capabilities adapted for the IoT, an estimated 25% of attempts to utilize IoT data will be abandoned before deployment ever occurs.”
Recently, more solutions providers have been turning to Edge Analytics, in tandem with the cloud, in order to better manage floods of IOT data. The Swim research team has compiled some of the best insights about Edge Analytics, in order to illustrate why an Edge Analytics strategy is critical to building a robust IOT solution.
What is Edge Computing?
Edge Computing is the processing of data at the “edge” of a network, which can be either embedded on or deployed near field devices. In other words, edge computing is a distributed computing model that leverages the various edge devices on a network, in order to more efficiently compute IOT data as it is created.
As Moore’s Law continues to provide cheaper (and smaller) computing power, individual IOT devices are becoming their own datacenter. Edge computing provides the networking framework to leverage these “data centers on the edge,” allowing developers to compose applications logically, and compute time-sensitive data on the edge, while pushing refined data to the cloud for further processing. By performing analytics at the edge, IOT project managers can benefit from decreased application latency, lower storage and maintenance costs, and a clear path for scaling an IOT application.
Edge Analytics Benefit #1: Lowest Possible Latency
InfoWorld’s David Linthicum describes that “the core benefit of edge computing is to reduce latency, and as a result increase performance of the complete system, end to end. Moreover, it lets you respond to some data points more quickly, such as shutting down a jet engine that’s overheating, without having to check in with a central process.” Because data is computed at the edge, it allows application developers to take advantage of local compute cycles, without incurring network latency. This enables developers to leverage data in real-time, without waiting for the cloud, for use in applications like predictive maintenance, machine learning, and Operational Technology (OT) management.
Additionally, cloud processes can now be completed more efficiently, as cloud systems have less raw, irrelevant data to sort through and they can instead work on highly structured, context-rich data. By providing refined data to cloud applications, Edge Analytics architectures improves latency for the complete system, not just on the edge.
Edge Analytics Benefit #2: Decreased Storage & Operations Costs
Another clear advantage of performing Edge Analytics is that applications can be modeled according to latency requirements. Time-sensitive data can be acted upon locally, and raw or irrelevant data can be reduced before transmitting to the cloud. This leads to a significant reduction in the amount of data sent to and stored in the cloud. Only the reduced data is stored in the cloud for further analysis, decreasing the bandwidth and storage requirements for an application, and thus lowering operations costs.
Furthermore, by performing Edge Analytics, you truly have real-time view of your IOT devices. This empowers OT professionals to more efficiently maintain deployed devices and ensure consistent uptime, as Edge Analytics can trigger real-time alerts for outages, or provide real-time data streams for machine learning and predictive maintenance initiatives.
Edge Analytics Benefit #3: Linear Scalability
Edge Analytics architectures can scale linearly as IOT deployments grow. Gartner describes that “with the highly distributed architectures required for most IoT solutions (many things, many places where data is generated, many platforms on which data is processed, and many consumption points to which data must be delivered), the historical approach to centralized collection of data is under pressure. Organizations must support a more distributed data architecture, because IoT solutions are inherently distributed.” As IOT applications are inherently distributed, it is logical to process IOT data in a similar fashion.
Edge Analytics architectures utilize the compute power of deployed devices, with each device auditing and learning on its own data as data is created. As more devices are added, compute power also grows. This reduces pressure on centralized cloud systems, as edge devices assume some of the burden for performing analytics functions. With a more effective division of labor, IOT deployments that include Edge Analytics can scale much more efficiently and reliably compared with cloud-only IOT deployments.
Learn how Swim ESP™ can empower IOT applications with Edge Analytics, to optimize and alleviate your IOT data overload. Swim ESP can help transform and manage your IOT sensor data using Machine Learning on your edge devices. Transform your flood of data into actionable insights.