As industrial enterprises across the globe look for new ways to improve productivity and provide better service, many companies are adopting IoT solutions that allow them to increase productivity without compromising safety or security. Enterprises that plan on developing IoT deployments need to choose an effective IoT application enablement platform (AEP), which allows them to build a successful and scalable IoT solution.
The key to enterprise IoT success is working with a vendor that provides robust IoT edge capabilities. Edge computing allows you to take the data from your devices and sensors to a nearby computing device that processes and analyzes the data rather than sending it back to the cloud. Below, we’ll expand on a few of the reasons why edge capabilities are critical to enterprise IoT success.
3 Reasons Why Edge Capabilities are Critical to Enterprise IoT Success
IoT platforms with edge capabilities help industrial enterprises address some of the unique challenges that the Internet of Things can present. Here are just a few reasons why enterprises should look to partner with an IoT solutions provider that offers IoT edge capabilities:
1. Enterprises need reliable and efficient IoT solutions that integrate into their existing environment.
Though many companies build their IoT devices from scratch, it is far more common for industrial enterprises to integrate IoT solutions into existing devices. Known as brownfield development, integrating connectivity and data collections into legacy devices presents a wealth of challenges that can hold many enterprises back from leveraging the possibilities that these devices present.
IoT platform providers with edge capabilities help address some of the issues that result from adding IoT technology to legacy devices. An IoT platform with a rich bidirectional integration framework helps integrate both the contemporary and legacy equipment. Platforms that offer a broad library of native connectors to legacy and modern devices will reduce the need for custom coding, saving enterprise organizations time and resources. For innovative, mature systems, the libraries of drivers make connecting at the edge a configuration exercise, not an integration.
2. Enterprises need to ensure enterprise-grade reliability with redundancy and resiliency in case of failure.
Enterprises need to ensure high reliability of their industrial systems by maintaining sufficient redundancy and component resiliency. The higher the uptime in an enterprise setting, the better the organization can utilize its resources, increasing output with less waste. New hardware and software in the IoT network has the potential to introduce new points of failure.
Edge capabilities allow enterprises to ensure that their new IoT technology maintains the reliability of their systems. Using self-contained edge nodes, IoT platform providers ensure that connectivity failures do not affect on-premise operations. This typically requires a gateway that runs edge services and applications while also providing a way to get data from the edge to the cloud. This helps address the challenges of unreliable connectivity that can spell disaster for enterprise organizations.
3. Enterprises need to get maximum value from data while minimizing bandwidth use and reducing costs.
The sensors on industrial equipment can generate terabytes of data within just one day of operation. Storing every piece of data can become costly. However, enterprises want to ensure that they are getting the most value from the data that they do collect. While this real-time data can be vital to business processes, it becomes a challenge for enterprises to process large amounts of data remotely with bandwidth constraints. Not to mention, the costs of data transfer can become quite high.
Working with an IoT platform provider that offers data processing capabilities at the edge device helps ensure that enterprises are getting the most value from their data in environments with bandwidth and cost constraints. Platforms that provide edge capabilities enable data processing near the source, getting rid of the need to pass the raw data back to the cloud, which can be costly and sometimes impractical.