// ROBOTICS AND SMART FACTORIES TERM
Edge Analytics
Performing data analysis directly on devices or sensors at the 'edge' of the network, close to where the data is generated, instead of sending it all to a central cloud. This reduces latency and bandwidth use.
TECHNICAL DEFINITION
Edge analytics involves processing and analyzing data directly at the network edge, on IoT devices or local gateways, to extract immediate insights, reduce latency, conserve bandwidth, and enable real-time decision-making without transmitting all raw data to a centralized cloud.
BACKGROUND
The Fourth Industrial Revolution, also known as 4IR, Industry 4.0 or the Intelligence Age, is a neologism describing rapid technological advancement in the 21st century. It follows the Third Industrial Revolution. The term was popularized in 2016 by Klaus Schwab, the World Economic Forum founder and former executive chairman, who asserts that these developments represent a significant shift in industrial capitalism.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Real-time edge analysis
- Local analytics
- Distributed analytics
USAGE NOTE
Edge analytics is critical for applications requiring immediate responses, such as real-time quality control or safety monitoring on the factory floor.
DEVELOPERS
Organizations developing technology related to Edge Analytics.
Siemens provides a comprehensive portfolio for industrial edge computing, enabling data processing, analytics, and AI applications directly on machines and production lines to optimize manufacturing processes.
Rockwell Automation offers FactoryTalk Edge Gateway and other solutions that bring real-time data collection and analytics to the plant floor, enabling faster decision-making and operational efficiency in manufacturing.
PTC's ThingWorx platform includes robust edge analytics capabilities, allowing manufacturers to process and analyze IoT data at the edge for predictive maintenance, quality control, and operational insights.
AVEVA provides a range of industrial software solutions, including edge-to-cloud data management and analytics platforms that enable real-time operational intelligence and improved asset performance in manufacturing.
IBM offers Edge Application Manager and related services that enable secure and autonomous operations of AI, analytics, and IoT workloads at the edge, supporting industrial use cases for optimized performance and reduced latency.
Microsoft Azure IoT Edge extends cloud intelligence to edge devices, allowing organizations to run AI, machine learning, and advanced analytics directly on industrial equipment for real-time insights and autonomous operations.
AWS IoT Greengrass brings cloud functionality to local devices, enabling local execution of analytics, machine learning inference, and data processing capabilities right at the industrial edge, even with intermittent connectivity.
Intel provides hardware and software solutions, including OpenVINO Toolkit and industrial IoT platforms, that power edge analytics for computer vision, AI, and data processing directly on industrial machines and gateways.
Schneider Electric's EcoStruxure platform incorporates edge control and analytics to provide real-time operational intelligence, enabling smarter decision-making, predictive maintenance, and optimized energy management in industrial environments.