ML Journal

CESMII’s John Dyck shares why manufacturers must align culture, improve data quality and bridge IT and OT with clear intentionality.

 

Penelope Brown, MLC: Hello, everyone, and welcome to this month’s Executive Dialogue. Joining us today is John Dyck, the CEO of CESMII—the Smart Manufacturing Institute, and also a member of MLC’s Board of Governors.

You may have seen John on stage at an MLC event, or at any other number of industry events, sharing his insights around how manufacturers can best leverage their operational data. And that’s what we’re going to talk about a little bit more today.

John, thanks for joining us.

John Dyck, CESMII—the Smart Manufacturing Institute: It’s a pleasure to be here. Thanks for having me.

PB: So let’s get into this a little bit. MLC research shows that most manufacturers have comprehensive policies for data security and privacy, but far fewer of those companies have one for data quality. Do you believe it’s important for manufacturers to have a policy in place that covers data quality specifically?

JD: I think this is a really important question, and one that deserves a thoughtful response. The hallmark of OT, and sort of the progenitors of OT data or manufacturing operations data has been one of innovation, but it’s also been one of doing what it takes to get the job done, and that often comes without a lot of structure, without a lot of rigor, and without a lot of oversight or policy.

And so, with that said, as we become more data driven, as we look to the value creation held out as an Industry 4.0 digital transformation characteristic, I think, or I believe firmly, rather, that the answer is yes.

“If you want to drive value creation through digital transformation, it’s going to take a culture where leaders at every level are moving away from making assumptions about their operations.”

 

Among other reasons, this would drive focus and would shine the spotlight on the competencies and the tools and the workflows that are required to support and fulfill those policies.

And it would also highlight the gaps between current practices and the desire to better enable a data-driven and strategic decision support or decision making. This is how we pave the way to address them and actually reach the value creation that I think we’re all aiming for through digital transformation.

PB: Getting into that—where manufacturers are seeking to be more data-driven and really incentivizing that—there could be a lot of things in place that may or may not encourage that. But do you believe it’s important, then, for executives at manufacturing companies to have that operational data collection piece or KPIs become sort of a business imperative or something that’s maybe tied to their performance evaluation?

JD: So I think it’s unquestionably about this being a business imperative. A data-driven culture doesn’t happen by accident, and it won’t happen without top-down intentionality and the support to actually enable it to happen.

If you want to drive value creation through digital transformation, it’s going to take a culture where leaders at every level, actually from the frontline worker to the very top of the organization are moving away from making assumptions about their operations, about their constraints, about their quality, about their performance, about maintenance, and moving towards a data-driven culture. We’ve talked about that forever, but we’ve truly been constrained by the lack of an approach—a structured approach—to creating policies and expectations for repeatable and trusted data. Because without that trust, you’re not going to get people actually believing in and then using the data to make important decisions.

So this is absolutely about a cultural transformation. This is about leadership at every level asking for, requiring and then building the capabilities to make that happen.

“You’ve got to build the DNA—the muscle memory—into the processes that enables a digital, data-driven culture.”

 

PB: Yeah, it’s been interesting. I was doing some reading on, historically, how manufacturers have been looking at data or talking about data for about the last 20 or 25 years. Around the early 2000s, it was sort of this talk about, “Well, you know, businesses are losing a ton of money on their data.” There really was so much that was unknown at that time. And then fast-forward about 10 years talking about big data. And now it’s basically, how do you have data in place to take advantage of AI and some of these really new exciting technologies.

But a lot of manufacturers are hitting a roadblock on that journey because of their issues with their data quality and consistency, and, as you say, being able to be repeatable and trustworthy. So how can manufacturers create a roadmap or a good strategic plan for improving and guaranteeing their data quality?

JD: Yeah. So, again, this requires strategic intent. And this requires a journey. And this requires a clear understanding of where you are today.

I think one of the really interesting things that we found here at CESMII, the Smart Manufacturing Institute, is that—while virtually every business has a business strategy and mission—what they don’t have is a manufacturing strategy and a digital transformation strategy that aligns manufacturing operations with that intent or with that strategic mission or vision.

So from our perspective, we’ve identified that as a major gap and have tried to build the competencies, the tools, the assessments, the maturity models and the ecosystem—the actual people that can bring this about for manufacturers, small and medium and large. And so I think this is something that needs to be spotlighted. This is something that, again, back to our previous conversations, will only happen with the right combination of understanding at the grassroots level and the intent to drive a cultural transformation as well as the top-down leadership that’s going to push and support and provide both the insistence on data-driven behaviors and business processes and then provide the support to make sure that happens.

PB: There’s a ton of conversation around “so much data,” right? It’s so ever-present within a manufacturing ecosystem, and there’s so many things that could be collected and analyzed. How can manufacturers really be sure they’re on the right track and become confident that they’re really collecting the right data that’s going to have impact?

“Once you trust the data, you’re going to highlight or really underscore the gap between what you assume to be true and what really is true.”

 

JD: It’s a great question. And I think there’s a really simple answer.

There’s a highly effective way to get there. You identify a specific business process that will impact the business in a meaningful way and run sort of the current manual process—the manual workflows—in parallel with a new digital decision support process, a digitally enabled system and process. And you’ll highlight very quickly whether or not you’ve got the right data, whether it’s contextualized the proper way, and whether over time it’s trustworthy.

And you’ll highlight too, immediately, the distinction between what the assumptions are in today’s more manual culture or manual, data-driven, gut-feel instinctually driven decision making. And this new data-driven process.

Once you trust the data, you’re going to highlight or really underscore the gap between what you assume to be true and what really is true.

So this question about what must be true, and then driving the business processes to actually deliver that is a proven way to make that happen.

And, by the way, it’ll take a while, there’s going to be hiccups and struggles along the way. But that’s how you develop the rigor. That’s how you develop the competencies, the tools. You can’t give up quickly and just say, “That didn’t work. We didn’t have the right data. It worked for this shift, but it didn’t work for the previous shift.”

You’ve got to build the DNA—the muscle memory—into the processes that enables a digital, data-driven culture, and over time with the right stick-to-itiveness and with the right leadership support to understand the gaps and have patience with the process along the journey, you’ll get there.

PB: So just one more question. Talking about more of the team and people side of things, CESMII has been doing a little bit of content and work—and probably a lot of content and work—around this specifically.

But you know, a lot of manufacturers are still asking the questions of how they can bring together their IT and OT teams. You know two teams that historically have had some very different objectives and speak different languages. What are some ways that manufacturers can successfully bring together those teams, especially with a focus on building that strong data foundation?

JD: Your opening comment brought to mind an interesting  discussion we had 20 years ago. I was the chairman of the board at Mesa International. We had an annual event, and we debated the theme of the event in 2005 as potentially being about OT/IT convergence. And at the time we decided not to, because we’d been harping on that for the previous two or three years, and we figured that by 2005 this was kind of passe.

But here we are, 2025, and fundamentally in the same spot, and I think it’s really important to understand the sort of root cause or the underlying systemic reasons for that.

“People are coin operated. People will do what they’re paid to do. And our current challenges that clearly exist between OT and IT are a function of those basic human natures.”

 

And it’s not surprising. But it kind of comes down to both organizational structure and incentive.

People are fundamentally going to do what they’re paid to do and what their leadership expects from them.

And in most cases still today, OT and IT each report to executives that come together at the CXO level. And they’re almost always incentivized in ways that drive them apart.

And until you realize that and understand that it’s more than just kind of taking a few folks from OT and from IT, and putting them together in an org chart, you really need a strategic look at your organizational structure, how that group is incentivized, and where the accountability for their success sits within an org chart.

It sounds trite, but it’s absolutely true. People are coin operated. People will do what they’re paid to do. And our current struggles, among other reasons, I think, first and foremost, our current challenges that clearly exist between OT and IT are a function of those basic human natures.

As you pointed out, those are a lot of the things that we’ve been working on within our membership and with our partners here to work on and drive focus to those important organizational structures, instead of incentivizing models and compensation models.

And then again, you need the leadership support that insists on solving this once and for all. There have been many attempts over the past few years to make this happen. Let’s learn from those and make sure the organizational expectation is for these two domains to be converged and work together to accomplish the organization’s desired outcomes.

PB: Maybe by the year 2045 we will have solved this.

Well, John, thank you so much for your time and your insights today. I really appreciated getting to talk to you a bit.

JD: My pleasure. Thanks for inviting me. M

Portions of this interview have been edited for clarity and length.

 

About the Interviewer:

 

Penelope Brown is Senior Content Director for the NAM’s Manufacturing Leadership Council