The Internet of Things (IoT) is already transforming the way organizations do business and run processes. The ability to get real-time insights from connected “things” and utilizing this information in business processes can help organizations create new business outcomes.
IoT’s potential to transform areas including operations, maintenance, supply chain, and safety is well understood. Oil and gas companies, which traditionally hold geographically dispersed assets, also stand to gain from this technology.
While much attention has focused on the application and device side of IoT architecture, edge technology has not yet been fully leveraged. It is important to understand the role that edge intelligence can play in the IoT architecture, as it can help identify opportunities for better results and lower costs.
Achieving situational awareness with edge processing
As IoT becomes more pervasive and more devices are connected, it will be increasingly important to reduce the noise-to-signal ratio by finding a way to cleanse, filter, and enrich the data collected by sensors for use in business environments. It’s estimated that 45% of IoT-created data will be stored, processed, analyzed, and used close to—at the edge—of the network. Edge refers to the periphery of the enterprise, in devices where sensors are located. The sheer volume of data coming from sensors can obscure many intelligent insights if the data is not cleaned, filtered, and given business context before it is used in business processes.
Intelligent edge processing allows businesses to use storage, streaming, persistence, and analytics as competitive differentiators by moving business logic closer to its point of origination—the edge.
Edge thus enables situational awareness, which refers to the use of intelligence to understand what’s happening in the moment to support decisions. Intelligent processing at the edge enables situational awareness by allowing companies to use available intelligence to make decisions that support business process execution. Businesses can consider the present state and possibly predict future outcomes before making decisions or taking action.
The benefits of edge processing
The key value proposition for intelligent edge processing is that it enables data collection and local processing at the edge of the enterprise and sends results to a centralized digital core. In this effect, it helps businesses achieve the following goals:
- Massive reduction in data transfer, thus reducing demand on resources like storage and bandwidth/connectivity
- Combines information technology and operational technology to enrich business process with context-sensitive sensor data
- Removes the latency of sending data on a round-trip journey from the edge to the enterprise and back again.
- Enables response times 100 times faster than that of cloud-based processing
Consider the example of a driverless car, where data captured through images must be analyzed to provide guidance such as speeding up and slowing down. Delays of one second or less could be lethal. For that reason, data must be analyzed and processed locally. Relevant data can always be pushed to the cloud to carry out analytics and improve the responses in the long term.
Leveraging the 4 A’s of intelligent edge processing
Intelligent edge processing brings together OT (operational technology) and IT (information technology) to enrich sensor data by giving it business context. It simplifies common IoT processing patterns and extends the digital business core to enable immediate action. For example, intelligent edge processing can trigger real-time safety alerts using data processed from multiple sensors.
Edge processing stores data locally instead of sending every bit of information to a cloud platform. With IoT, billions of sensors produce a continuous stream of data, creating volumes that traditional data storage technologies would be unable to manage. Intelligent edge processing manages these large data volumes by storing only what is relevant. Availability of this data at the local level means businesses can push it to the cloud or discard it if it’s not relevant.
Business context drives situational awareness and actionable business processes. While sensor or OT data can provide minute-by-minute details of equipment performance, it lacks the larger perspective, which usually is part of the enterprise system. For example, before scheduling maintenance, decision-makers need to know labor costs and availability of work orders or automated parts procurement. Intelligent edge processing can access this information help business leaders execute business process locally with operational and business context. Intelligent edge processing brings together OT and IT to enrich sensor data with business context, extending the digital business core to enable immediate action.
Edge processing provides the ability to analyze streaming data from sensors. Algorithms and a syncing architecture with local storage allow businesses to process IoT data locally, where it’s created and needed for consumption, and transmit it securely, efficiently, and opportunistically. Analysis at the edge reduces the amount of sensor data that is transmitted and thus the cost associated with data transmission. It delivers insight where and when you need it. Algorithms and processing rules can be created in the cloud and pushed to the edge to be executed locally. Multiple parameters can then be analyzed and necessary actions taken based on algorithms.
With intelligence edge processing, responses can be automated. For example, intelligent edge processing can trigger real-time safety alerts using data processed from multiple sensors. Response commands might be to shut down equipment or to issue warnings or alerts. For example, if the temperature of a boiler goes beyond 95 degrees C, edge processing can initiate a command to shut down and simultaneously create a maintenance work order by capturing the business context information and the relevant sensor/edge data to initiate the repair. Rather than sending all IoT data related to the work order, only the critical sensor readings are aggregated and sent to the cloud, reducing overall data volume.
In an IoT scenario, it is not practical to ramp up the number of disk drives to store all the data from all the sensors. Instead, compute power is moved to the edge, closer to where data is originated. This is where the real value of the IoT lies. Scaling the cloud to the edge for latency-sensitive use cases and business processes saves time, boosts response time, reduces data storage and transmission costs and optimizes overall business outcomes.
For more on the power of IoT, see Investing in Trusted Data.