Data Standards Won’t Solve Your Interoperability Problems

May 11, 2023
Industry standards are important but, on their own, they cannot tell a use-case driven story. However, when paired with an industrial data ops solution, they can help expedite data modeling to deliver contextualized, intelligent insights.

The efforts of standards organizations like OPC Foundation, Eclipse Foundation (Sparkplug), ISA, CESMII and MTConnect represent a significant step forward for the advancement of Industry 4.0 in manufacturing.  

But industry standards only go so far. Standardizing the device-level data into structures is key, though only the beginning. Here are four key reasons why you still need an industrial data operations (data ops) solution in your system architecture—even with the introduction or evolution of new standards.

1. You’re dealing with machine and vendor variability.

Standards bodies are made up of vendors and users in an industry. As the standard is being defined, variances are allowed for vendor machines with unique capabilities, limitations and use cases. While the intent is flexibility, the result is often ambiguity. It’s typical for vendors to implement the same standard slightly differently. Historically, vendors have refined their systems and changed data models over time to suit their needs.  

As a result, even minor variations in datasets require human interaction to link these machines to other systems in the network and automate dashboards or analytics.

An industrial data ops solution can help you connect to a wide range of sources, including equipment or controllers, smart devices, sensors, and systems, without writing or maintaining code. If the input data is not standardized, it can be modeled and transformed to the governed data standard for the use case.  

2. You’re viewing individual data with no relationship context.

Think about a manufacturing line with multiple machines. The machine standards address the data for each machine independently, not the combination of the machines or any custom automation connecting the machines. When analyzing operational metrics, bottlenecks or quality root cause for a production line, specific information from each machine, test stand and sensor should ideally be assembled into a single payload for that line.

An industrial data ops solution makes this possible. You can merge and model data from multiple machines and then correlate the data by logical use case. This systematic approach of building data models for the use case greatly accelerates the use of this information by line of business users who are less knowledgeable of the machines and line layouts.  

3. You’re looking at more than just device data.

You can’t make strategic decisions if you’re not linking your machine data to other systems across your organization. This includes enterprise applications, such as your ERP system, and your manufacturing databases (e.g., SCADA, MES, Historian, QMS, LIMS, and CMMS).

Your industrial data ops solution should connect to virtually any system in your organization and combine information from these systems with machine data—bridging the gap between operations technology and the rest of the business.  

4. You’re not getting the data you need, when you need it.

Information overload is a real problem in the Industry 4.0 world. Industrial data is nearly infinite in both the volume of data values and the frequency at which they can be acquired. Understanding what is needed—and when—is critical. Sometimes data is needed at a cyclic rate, once per second. Other times, you may need an event-based feed to identify cell production complete, defects or machine performance issues.

By using an industrial data ops solution to define the desired data payload and its event or frequency, you create a more efficient decision-making process, and you minimize cloud costs because you only store and process the data you need.

Standardized models are important to our industry because they provide a baseline dataset. But industry standards only go so far. They cannot tell the use case driven story. However, when paired with an industrial data ops solution, they can help expedite data modeling to deliver contextualized, intelligent insights that drive strategic decision making. As you digitally transform to Industry 4.0, use your data to tell stories that solve business problems.

John Harrington is chief product officer at HighByte.

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