The edge is focused on bringing computing as close to the data source as possible. The edge means running fewer processes in cloud and enterprise systems and moving them closer to the devices generating data, such as a standalone computer, an IoT device, or an edge server. Localizing computing minimizes the amount of long-distance communication between a client and server, thus transforming the way data is handled, processed, and delivered.

Gartner estimates that installed IoT endpoints in the manufacturing and natural resource industries will grow to 1.9 billion units by 2028. Data volumes are only going to continue to multiply, especially in industries undergoing rapid digital transformation such as manufacturing, oil and gas, energy, and transportation. Organizations need a way to manage this data explosion at the edge and the many associated challenges including complex systems, latency, insufficient bandwidth, and rising costs for storing and processing data in the cloud or data centers.

Industrial edge computing refers to the process of connecting all assets used in manufacturing, oil and gas, energy, transportation and more. Industrial edge computing analyzes all of the data at the asset and processes it instantly for real-time analytics or to integrate optimized data into cloud systems for further processing.

The industrial edge has matured rapidly, and most factories have achieved some level of connectivity. However, they typically include multiple disparate systems, hundreds of heterogeneous devices, and legacy technologies that do not work together effectively. So while the industrial edge may be networked, data is not flowing freely from the edge to the cloud and back again for true real-time intelligence. Companies need a simple way to connect legacy and disparate systems to extract and make use of the data.

Edge and cloud technologies need to work together. To suggest one offers greater value over the other is simply not true. The edge is valuable for its ability to process high-volume data in real-time and handle complex analytics at the data source. The cloud is valuable for its ability to aggregate and analyze volumes of data from all data sources, including the edge.

Many companies are either sending all of their data to the cloud or keeping it at the edge, they don’t know how to effectively do both. Cloud computing has its benefits for long-term analysis and large-scale deployment but is limited by bandwidth and often collects vast amounts of data but only uses a small portion. The value of industrial edge computing lies in taking action at the edge where it has the greatest impact and zero latency. The industrial edge activates real-time analytics like asset utilization, asset uptime and downtime, capacity utilization and more for immediate business decisions that improve quality and processes.

The edge has three main components. Edge connectivity is the ability to connect to any industrial system and collect and normalize data for immediate use. Edge intelligence is concentrating data processing and analytics functions at the edge to take action and derive value at the data source. Edge orchestration is the ability to create, deploy, manage and update edge applications.

The industrial edge is most powerful when all three components are unified. Some companies are dabbling in the edge with connectivity alone, but the true value is not realized until data is collected at the edge, analyzed for real-time analytics, and applications are orchestrated at the edge for further processing and value. The ideal edge strategy takes all three of these components into account, activating the edge for device connectivity, data collection, real-time analytics, data integration, application orchestration, and machine learning runtimes.

The vast importance of the edge is beginning to come to light as more industrial use cases are enabled. The edge powers preventative maintenance, condition based monitoring, OEE, vision systems, quality improvements and more. Edge data can also power more advanced use cases like artificial intelligence and machine learning in the cloud. The intelligent edge is powering significant operations and process improvements.

As companies continue to embrace digital transformation, Industry 4.0, Smart Manufacturing and all of the advanced use cases those initiatives bring with them, the industry is recognizing the importance of the edge and the cloud working in harmony to drive intelligent business decisions. Companies at the forefront are adopting modern edge platforms to drive these initiatives with a unified solution that enables the three facets of the edge – edge computing, edge analytics, and edge intelligence.