TL;DR

• Many OT teams are overwhelmed by unstructured data.
• Data silos limit visibility and prevent actionable insights.
• Modern edge infrastructure improves data access and context.
• Edge controllers enable reliable connectivity in remote environments.
• Low-latency data processing drives performance and autonomy.

Why this matters now

As organizations began their digital transformation journeys years ago, many were laser focused on collecting data, but less concerned about how they collated and organized that content. In the beginning, when sensors were expensive and limited, such a strategy was manageable. Today, however, sensors are affordable and widespread, and many operational technology (OT) teams are drowning in data—data that could unlock significant performance gains if it were more usable.

Emerson’s Manish Sharma explores this challenge in a recent article in Control magazine. The core issue, he explains, is the silos created as more systems are added, and how they limit the seamless communication required across operations:

“IIoT started with wired connections and messaging protocols, and linked devices including controllers that were each in their own silos.”

Those silos, originally built by design, have become barriers to the connectivity modern operations demand.

Takeaway: Data silos are now a primary barrier to extracting value from industrial data.

Today, the expectation is different. Systems must communicate seamlessly, and data must be aggregated and analyzed to provide clear insights across the plant and enterprise.

Better infrastructure, better data delivery

A key strategy for overcoming this challenge is modernizing infrastructure to support high-fidelity data availability.

Industrial PCs designed as virtualized edge controllers—such as Emerson’s RX3i CPE400 and CPL410—enable both real-time control and edge computing within a single platform. This combination allows organizations to collect, process, and contextualize data much closer to the source.

Manish highlights a real-world example from the oil and gas industry. In the Gulf of Suez, an operator sought to automate flare-gas monitoring and emissions tracking to generate carbon-offset credits. However, limited connectivity in the remote location made continuous cloud data transmission difficult.

The implementation of an edge controller with PACEdge software solved this challenge. The system collected data across multiple protocols and transmitted it via a cellular gateway using MQTT, ensuring reliable connectivity while maintaining flexibility.

“In addition, CPL410 provides geolocation and real-time data tagged with relevant asset information, including datasheets, install and maintenance records, and photos. This enabled a robust data trail that increases the client’s chances for successful credit verification, and avoids costly, onsite visits, manual data recording and error corrections.”

This demonstrates that the value is not just in connectivity—but in context.

Takeaway: Edge systems transform raw data into contextualized, actionable information.

Even in the case of intermittent connectivity, the system stores data locally and forwards it when connections are restored, ensuring continuity and reliability.

The success of the approach led to replication across multiple wellheads, reinforcing its scalability and impact.

Low latency, high results

One of the most important advantages of edge control solutions is the ability to dramatically reduce latency. By bringing computation closer to the process, these systems enable faster interaction between real-time control and higher-level analytics.

“Edge controllers arrived pretty recently, and they can close a loop in milliseconds, which is almost real time.”

This is a significant improvement over traditional devices with loop cycles that can take five or ten seconds,

Faster loop execution enables tighter process control, better responsiveness, and more effective integration with advanced technologies such as analytics, vision systems, and optimization tools.

Takeaway: Low-latency edge processing enables faster, more precise operational decision-making.

Building toward autonomy

Ultimately, edge-enabled infrastructure is not just about improving data flow—it is about enabling a new level of operational performance.

By integrating real-time control with contextualized data and analytics, organizations can reduce errors, improve sustainability outcomes, and increase throughput across their operations.

As these capabilities mature, they also lay the foundation for more autonomous operations—where systems can respond intelligently to changing conditions with minimal human intervention.

Takeaway: Edge computing is a foundational enabler of autonomous, high-performance industrial operations.