Use context with IIoT to provide automation value

The Industrial Internet of Things (IIoT) has a lot of potential, but without context and a clear goal, value for companies will be less. See 8 ways to move from data to information.

By Chris Vavra April 11, 2019

Addressing misperceptions on Industrial Internet of Things (IIoT), presenter Jeremy Drury, vice president, IoT diagnostics, pulled an audible at the start of his presentation on April 10 at Automate 2019. Instead of providing sense about the IIoT, he said it was more important to provide context. “Defining context of IIoT in automation,” for him, is the key to everything he does and everything he believes the IIoT can provide.

In contrast to many views expressed, Drury seems to have a more realistic approach about the IIoT and its potential. “I’d be lying if I said IIoT is easy. It’s been pretty challenging for many of us. We’ve been at it for a few years and taken quite a few punches,” he said.

There’s been a lot of talk, he said, about Industrie 4.0 and how all this wonderful data can transform manufacturing. There’s one problem. “I would argue many of us are still struggling with Industry 3.0,” he said.

Thinking ahead in manufacturing is great, but getting to Industrie 4.0 created a gap, which is a challenge for companies looking to move forward. “There’s not a clear bridge on how to get Industrie 4.0 from where we are today to close the chasm,” Drury said.

There’s a lot of potential for the IIoT and for companies to save money, but it’s a zero-sum game, Drury said. Saving time by reducing the time it takes to perform a task is meaningless if it’s replaced by having the engineer spend all their time looking at dashboards and trying to interpret data.

This is where context comes in. How do you take all that data and try to make sense of it? How can you give it meaning to manufacturing so they can take the next step to save money?

8 ways to move from data to information

Drury said he works for a data company, but what does that mean? What does it mean to make data? If companies want to make data, it’s really the beginning of an eight-step process:

  1. Make data: Gather data from sensors and other devices.
  2. Move data: Route the data through industrial gateways, wireless modules, and data acquisition devices.
  3. View data: Put the data on dashboard interfaces and databases.
  4. Secure data: Ensure the data is securely located whether it’s local or on the cloud, and take steps to safeguard it through cybersecurity and blockchain protocols.
  5. Use data: Allow the user to access the data through digital twins, rapid service response, and more.
  6. Scale data: Take the data beyond a proof of concept (POC) and use it to provide the context companies sorely lack and use it to provide a business model.
  7. Share data: Use the data to form strategic partnerships and build out an ecosystem.
  8. Analyze data: This, Drury said, is really where Industry 5.0 because now all these bits of information are being analyzed to improve decision cycles through predictive analytics and Big Data.

The key, Drury said, is the fourth and fifth steps, which is where the information goes being from enabled and integrated to being used. That’s where the shift to providing context begins.

Validating the information

All that information is great and wonderful, but what does it mean? That’s a challenge Drury sees a lot in his work. “So what?” is a question he gets a lot. And it’s a fair question. How does all this information help the company? How is the data being gathered and correlated being put together to make it all work? Where’s the context?

It’s important, Drury said, for companies to look beyond the big picture and examine where the data can help improve operations in practical, realistic ways. He cited a case on the Automate show floor where IIoT data was being used in a practical sense at the Emerson Aventics booth with their valve. He cited the data being compiled as a practical example of how the IIoT can provide context for companies because it’s gathering all the data and putting it to use.

Using the data for a better return on investment (ROI) to solve challenges, such as harmonizing the production process and training with clear baselines. Regardless of what the goal is, there needs to be one. Otherwise, all this data is meaningless.

“If you’re not being thoughtful about your ROI context and understand what you’re going after, you’re not going to get out of the POC stage,” Drury said.

The savings are out there and Drury, in spite of his more realistic viewpoint, said he believes companies can save money today if they understand what they’re going after.

“All of us add more value to what we do today and how we can save money today,” Drury said. “That’s how we’re going to make this Industrie 4.0 thing work.”

Chris Vavra, production editor, Control Engineering, CFE Media,

Author Bio: Chris Vavra is senior editor for WTWH Media LLC.