How to identify and process data for usable, actionable information

Build essential capabilities into projects from the start

By Ian Verhappen

As the number of intelligent devices able to communicate digitally continues to increase, which is a precursor and requirement of the Industrial Internet of Things (IIoT), one of the challenges is managing all the resulting data. Managing the data and associated flows, being sure it's captured and sent to the right place, is only one part of the challenge. Identifying and processing the data, so it becomes usable information on which actions can be taken, is why it's collected in the first place.

For reference, the ISO definition of data is “sampled measurements of a physical quantity” (ISO 2041:2009). ISO defines information as “facts, concepts or instructions” (ISO/TS 13399-4:2014) or, per ISO-9000:2015, meaningful data.

Without a plan for identifying and capturing information, the challenge becomes how to determine, select and find the proverbial needle in the haystack. A typical intelligent device or transmitter contains in excess of 300 parameters, so part of the challenge starts with determining which of those potential pieces of data are relevant to your facility and project. Many parameters will be factory settings and only relevant to the device manufacturer; others will be the default device configuration; while a few (likely 10-20%) will be of interest or need to be modified as part of the project over its lifecycle. Converting data to information starts with the initial design, and having the systems, equipment and tools to manage the data you expect to be generated.

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Engineers know how to plan, we just forget sometimes because our focus is on the end goal. So some steps get overlooked or there's no time in the budget. Remember, a mistake or oversight at the start of a project can take 1,000 times the effort to repair at the end of the project. Though the folks doing the heavy lifting at the front end of a project tend to be those with more experience and a heftier price tag, the money saved at the end returns that cost many times over.

Engineers know how to plan, we just forget sometimes because our focus is on the end goal.

Back to the plan. A good start is identifying use cases to confirm what you want to and could achieve. Then it’s time to prioritize to determine what you can achieve within the constraints. It may be that all you can do with your budget is identify the key parameters to transfer from your devices; create the tools to make that a straightforward and repeatable process that can be done by a first-year technician; and provide the system to capture and store that data. Analysis will have to come later or from another project, but you'll have a way to get data when you need it.

The minimum field hardware is to have all your I/O cards support your field devices, so you can talk with them from a system with the capability to capture data without human intervention after it becomes operational. For wireless field networks or any fieldbus system that communicates digitally, this bidirectional communication is a given, though as indicated, you still need to pick and choose what goes where.

The other end of the spectrum is an autonomous, integrated system that, once running, is almost entirely machine-to-machine communications up to and integrated with the ERP and maintenance systems. It generates work orders, maintains inventory and modifies the way the process operates when the signals used for control begin to deteriorate. Not too many folks know how to get something like this even started correctly, never mind build it. However, it's still part of the IIoT vision.

ISA-108 is trying to provide some guidance on how these systems and work processes should look to enable capturing and sharing best practices across an organization. Future documents in the series define what some of the tools might look like and how they would work, such as device templates to identify the core suite of data on which to build the system.

All of us at one time or another have simply felt overwhelmed for one reason or another. Fortunately, the tools are becoming available to prevent that from happening with our intelligent control systems—provided we make the effort to change data to information from the start of our projects.

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