Existing IT and OT infrastructures typically don’t collect, store and analyse data at the edge. They instead either send this data to the cloud or to enterprise-level computing systems for storage and analysis, the domain of IT personnel.
A better solution, specifically for applications where access to data needs to happen quickly, is to perform data collection, storage and analysis at the edge using technologies designed to perform these specific tasks. The benefits of this approach include reduced latency, improved data security and more efficient use of bandwidth.
Implementing and maintaining edge solutions can be highly complex, but this doesn’t have to be the case if solutions are designed to be used by OT personnel and don’t require a high level of IT expertise.
Delegating to OT
Collecting, storing and analysing data are the three main edge tasks. Data collection is very familiar to OT personnel because they have been doing this for decades to automate operations, where collected data is fed directly to real-time control systems. Selected data from these real-time control systems can be easily transmitted to a local edge system for storage and analysis.
With newer industrial internet of things (IIoT) implementations, data collection remains the first step. The difference is this collected data is sent directly to an edge system for storage and analysis, instead of through the real-time control system.
Once data is collected, it needs to be stored. IT is very familiar with this task and can provide assistance as needed to OT personnel, but a better solution is to simplify edge complexity so OT personnel can do this on their own without comprising the IT/OT network infrastructure.
Analysis of field device data must be done by OT personnel because this task demands deep domain knowledge of operations. For example, deciding if an excursion from a setpoint represents a significant problem or just a normal operating state demands domain knowledge, as do most other analysis tasks.
So, two of the three main tasks performed by an edge system—data collection and analysis—require a high level of OT expertise. If data storage complexity can be reduced so it doesn’t require a high level of IT expertise, OT personnel can handle the entire gamut of tasks required for edge applications, freeing up IT personnel to concentrate their efforts on higher level tasks.
Cutting Complexity
Collected data often needs to be processed before it’s stored to filter out temporary blips, infer likely readings when there are gaps, compact data by only storing significant changes, etc.
After processing is completed, data must be stored securely. This is very important because it is often the most critical asset for a company, and it is the only asset produced by most IIoT implementations. Fault-tolerant availability is key to protect not only processed data, but also the actionable information created by analysis.
It thus becomes very important to invest in approaches with data collection, storage and security capabilities because edge technologies are often used in remote environments where there is limited or no staff, IT or OT, available to help fix a problem if something were to fail.
Once data is processed and stored, it often needs to be securely transmitted from the edge to the cloud or to other higher-level computing systems such as ERP, asset management, etc. Applying the right edge technologies can help simplify these data transmission tasks to the point where only minimal IT expertise is required for implementation.
The key to cutting complexity is implementing edge technologies that can be remotely managed while offering continuous availability. This allows companies to act quickly if a problem arises, keeping mission-critical applications up and running, which lets OT personnel handle most edge implementation and maintenance issues with IT personnel available to assist with any vexing problems that may arise.
Author profile:
Dave Laurello is CEO of Stratus Technologies
