The IT/OT Insider Podcast - Pioneers & Pathfinders

In this episode, Wybren van der Meer, a strategic data consultant, discusses the importance of data governance in industrial settings. He shares insights from his background in physics and experience in data management, emphasizing the need for a clear definition of data governance, the evolution of data practices in industry, and the role of trust and reliability in data management. The conversation also touches on practical applications of data governance, such as in coffee roasting, and the challenges of scaling governance practices across different plants. Wybren highlights the significance of starting small with governance initiatives while keeping the bigger picture in mind, and the necessity of engaging people in the process to ensure successful implementation.
Find Wybren on LinkedIn: https://www.linkedin.com/in/wvandermeer/ 
More on the Unified Namespace: https://www.youtube.com/watch?v=d1QeZWb6rt0
More on the Industrial Data Platform: https://www.youtube.com/watch?v=mdtY2Ks8F6M 
Learn everything about IT/OT Cooperation, Industrial DataOps and more: https://itot.academy 
More about The IT/OT Insider: https://itotinsider.com/ 

Chapters
00:00 Introduction to Data Governance and Wybren's Background
02:51 Understanding Data Governance in Industrial Contexts
05:59 The Evolution of Data Governance in Industry
09:12 Defining Data Governance and Its Importance
11:56 Implementing Data Governance: Challenges and Strategies
15:01 Data Governance in Coffee Roasting: A Practical Example
18:06 Scaling Data Governance Across Operations
20:52 The Role of Data Governance in New Projects
24:06 Overcoming Resistance to Data Governance
27:01 The Future of Data Governance in Industry


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Welcome to the first IT/OT Insider Podcast of 2026! We’re kicking off the year with someone who’s done it all: refineries, equipment reliability, process safety, even the postal industry (and found data at the heart of every role).
Kris Doering recently joined SaskEnergy, a government-owned natural gas transportation company in Saskatchewan, where he works on system modelling and asset planning. But before that, he spent years at the Co-op Refinery Complex as superintendent of refinery performance improvement, working on benchmarking, goal-setting, and deploying process safety software. His career also includes stints in equipment reliability, Lean Six Sigma at Canada Post, and early days implementing PI System for upstream gas producers.
What ties it all together? Data. And not just collecting it.
From Postal Sorting to Refinery Benchmarking
Kris’s career path is anything but linear, and that’s precisely what makes his perspective valuable. As he put it:
“Data has really been a common thread through the whole career. No matter where I worked, what field I worked in, it’s really been the thing that’s tied all of my roles together.”
His time at Canada Post might surprise those who don’t think of postal services as manufacturing. But as Kris explained, the parallels are striking:
“You’re getting things off of semi-trailers, you’re sorting mail based on barcodes, you’re dealing with advertising mail, newspapers, parcels from Amazon. There’s a lot of infrastructure and a lot of processes.”
Those early Lean Six Sigma projects at Canada Post became foundational for everything that followed. “That work really kind of prepared me for all of the other stuff that I’ve done,” Kris noted.
Leading vs Lagging: Why Process Safety Metrics Matter
Our conversation centred on process safety. This is a topic that doesn’t always get enough attention outside refineries and chemical plants, but has lessons for anyone working with data and performance management.
Kris worked extensively with process safety at the refinery, deploying HSE software and investigating incidents. He explained the critical distinction between leading and lagging indicators: “A lagging indicator is when something bad happens. A leading indicator is something that you can measure that you think will correlate to the outcome.”
But here’s where it gets tricky. As Kris pointed out, truly leading indicators—ones that predict future incidents—are extraordinarily difficult to design:
“The problem with trying to create a leading indicator for process safety is that, you know, there’s an infinite number of things that could go wrong and an infinite number of conditions that could exist out there.”
Instead, what most organisations end up with are proxies—measures of how well they’re managing known risks. And that’s not necessarily a bad thing, as long as you’re honest about what you’re measuring.
Front-Line Scoreboards: Making Data Visible Where It Matters
Another practical insight from our conversation was Kris’s experience with front-line scoreboards—physical boards where teams track their own performance metrics.
“If you’re tracking the right information and putting it on a scoreboard that is understandable to the people who are doing the work, then those people actually engage with it. They want to know how they’re doing.”
This isn’t about surveillance or micromanagement. It’s about giving people the context they need to understand their impact:
“They know that they’re there to do a job and they want to know if they’re doing a good job or a bad job... and how to be better at their job.”
The key is connecting individual behaviour to outcomes in a way that’s visible and actionable. It’s deceptively simple, but as Kris noted,
“Connecting individual behaviour to organisational performance is an inherently complex problem, and replicating it through an organisation is complicated, too.”
Complex vs Complicated Work
Towards the end of our conversation, we touched on an important distinction that anyone in industrial operations should understand: the difference between complicated and complex work.
Complicated work has known solutions—it might be difficult to execute, but the path is clear. Complex work, on the other hand, involves uncertainty, ambiguity, and problems that aren’t well-defined. As Kris put it:
“It’s so important not to complexify things. You must come to the simplest solution. And as you gain more knowledge, more skill, more experience, what ends up happening is you recognise how to make things simple and break things down.”
The secret? “A desire to not choose to take on too much for myself.” Sometimes the most skilled move is knowing what not to do 🙂
Further Reading
If you want to dive deeper into some of the topics Kris discussed, here are two excellent resources he recommended:
* HSG 254: “Developing process safety indicators - A step-by-step guide for chemical and major hazard industries” Available free at: https://www.hse.gov.uk/pubns/priced/hsg254.pdf
* API RP 754: “Process Safety Performance Indicators for the Refining and Petrochemical Industries” Available (subscription required) at: https://www.apiwebstore.org/standards/754 Annex I is particularly recommended for defining process safety data requirements.
* The “useless machine”: https://www.cbc.ca/news/canada/saskatchewan/useless-machine-maker-from-regina-gaining-worldwide-fame-1.1326579
* And you can find the book “Sooner Safer Happier” by Jon Smart in our Mini Book Library.
Stay Tuned for More!
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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com

Some guests make you pause halfway through the recording and think, “Okay… this one’s going to need a second listen.”
That was the case with Dan Jeavons, president of Applied Computing, formerly VP of Computational Science and Digital Innovation at Shell — and one of the people who has quite literally been shaping how data, AI, and physics come together in industry.
From ERP Reports to Foundation Models
He began, like so many, somewhere between spreadsheets and SAP.
“The biggest value of having an integrated system is the fact that you have an integrated data layer,” he recalls. “I didn’t like the systems much — but the data was really interesting.”
That curiosity led him from analytics experiments in R and MATLAB to building Shell’s first Advanced Analytics Center of Excellence — which, as he jokes, “was neither advanced nor excellent… but we got better quickly.”
Thirteen years later, he was leading teams across AI, data science, and advanced physics modeling — and wrestling with a problem that every industrial data leader knows too well:
“You either rely on physics and trade off flexibility, or you rely on statistics and trade off explainability.”
What AI Looks Like From the Plant Floor
Dan has worked across the energy value chain — from offshore wells to refineries — and says something that surprises many:
“From a data perspective, it all looks very similar.”
Distributed control systems, process historians… “whether you’re on a platform in the North Sea or in a petrochemicals plant, the data architecture doesn’t really change,” he says.
And that’s what makes the AI opportunity so big.
If every facility generates data in roughly the same way, then algorithms can be adapted and scaled — not rebuilt from scratch each time.
Why IT/OT Convergence Still Hasn’t Happened
At one point, we asked the question: Has IT/OT convergence really happened?
Dan didn’t hesitate:
“No. We’re only scratching the surface.”
He describes today’s operations as “a DCS at the heart of the operation, surrounded by siloed engineering processes — reliability, maintenance, safety — each with their own tool, using a fraction of the data.”
Adding AI layers on top of that, he argues, is helpful but incomplete:
“We’ve added a layer of intelligence on top of existing systems. But it hasn’t changed the work process yet.”
True convergence, he says, will come when AI doesn’t just analyze the work — it redefines it.
The Real Meaning of “Digital Twin”
Few topics create more buzz (or confusion) than digital twins. Dan gives one of the clearest definitions we’ve heard:
“A true digital twin must do three things: represent the physical world, be interrogable in real time, and run simulations that explain why and what next.”
That’s a high bar…
“The technology exists,” he says. “We just haven’t stitched it together yet.”
Change Management: The Hardest Part
Dan’s third “impossible problem” isn’t technical — it’s human.
“These facilities are extremely risky. They’ve run safely for 40 years. So when you say, ‘Let’s change everything,’ it’s a hard sell.”
He lays out the classic resistance:
* It works, don’t touch it.
* We can’t risk downtime.
* We’re here to deliver return on capital, not to experiment.
And yet, as he points out:
“Even with the way we run things today, we still have reliability problems, we still have safety exposure, and we’re losing expertise fast.”
His conclusion is blunt:
“Someone is going to figure this out — and when they do, they’ll be 50 % more efficient. If you’re not on that train when it happens… good luck.”
Rethinking the Cloud Debate
When the topic of cloud reliability came up (AWS outages, anyone?), Dan didn’t dodge.
“The idea that you’re safe because you’re air-gapped is a fallacy,” he said flatly. “Most OT environments are already virtualized — effectively private clouds. The question isn’t if you’re exposed, it’s how well you manage it.”
The challenge, he says, isn’t cyberthreats — it’s change management in the cloud era.
“Continuous deployment doesn’t work in operations. We need cloud architectures that respect industrial change control — and OT vendors who step up to modern security standards.”
From Use Cases to Foundation Models
Dan’s view of AI’s future is clear: we’re moving from narrow, use-case-specific algorithms to general-purpose foundation models that can reason across disciplines.
“Before 2023, companies built algorithms for individual problems: corrosion, valves, compressors. Now, the next generation of models will handle all of them because they understand physics, language, and time series together.”
He tells the story of Sam Tukra, his former colleague (now Applied Computing’s co-founder and Chief AI Officer alongside Callum Adamson) who figured out how to make those three domains “talk” to each other.
“He built an agentic system that cross-validated physics, language, and time series. I was equal parts proud, frustrated, and amazed. Suddenly, you realize — this is it.”
The result is Orbital, their platform that blends these layers — a system that can predict, explain, and reason across disciplines, from reliability to safety to economics.
Looking Ahead
Dan calls this convergence of physics and AI an “inflection point for industry.” He’s convinced that in the next decade, the companies who embrace it will operate differently — not because AI tells them what to do, but because it changes how they work.
So that means that we need to plan for another podcast in a year or so from now ;)
Thanks for listening!
Stay Tuned for More!
🚀 Join the ITOT.Academy →
Subscribe to our podcast and blog to stay updated on the latest trends in Industrial Data, AI, and IT/OT convergence.
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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com

Today’s guest has lived what most companies are still figuring out: how to turn fragmented systems, manual Excel work, and well-intended “shadow IT” into a coherent Industrial DataOps strategy that actually delivers value.
In this episode of the IT/OT Insider Podcast, we sat down with Rija Rakotoarisoa, Group IT Operations & Industry 4.0 Lead at Poclain Hydraulics, a French (international) independent group specializing in the design, manufacture and sale of hydrostatic / electrohydraulic transmissions: motors, pumps, valves, system for off-road or mobile machines and one of the global leaders in hydrostatic transmissions.
If you’ve ever found yourself trying to bridge IT and OT while juggling standardization, culture change, and budget cuts… you’ll feel very at home in Rija’s story.
From Developer to Industry 4.0 Leader
Rija started his career firmly on the IT side: a master’s in computer science, developer turned IT manager, working in a plant where his job was to keep systems running and people connected. Then came the shift.
“After five or six years, I felt like I had seen everything. I wanted to do something more than pure IT, something that had a direct impact on the business.”
So he went back to school, this time for a master’s in finance. Not because he loved accounting, but because it was his way to “remove the geek tag.”
“If you wanted to have more impact, you had to speak the business language.”
That change paid off. Rija became both IT and finance manager at one of the company’s plants and learned firsthand what happens when you put technology in service of the business. He used automation to help teams understand their own costs, improve efficiency, and cut the manual data entry that was eating up hours every day.
Lessons from Good and Bad Projects
In his later roles, including a global Industry 4.0 function, Rija saw dozens of digital projects across multiple plants. Some brilliant, others not so much.
“A bad example is when a company rolls out something top-down. They say, ‘This is the strategy, you must implement it,’ without asking the real problems at the plant. It takes time, money, and in the end, nobody uses it.”
Sound familiar?
The good examples, he says, start from the other direction. From real operational pain points.
“When you address the real problem in manufacturing - something that changes the day-to-day of the operational team - then they support you, they use it, and they apply it every day.”
It sounds simple, but as he adds, “it’s not.” It takes change management, communication, and people inside each plant who carry the message and help build local momentum.
Starting from a Digital Greenfield
When Rija joined Poclain Hydraulics, about 6 years ago, it was, as he puts it, “a digital greenfield.” The company had strong IT foundations (infrastructure, networks, ERP), but no consistent support for manufacturing systems yet.
“There were many IT/OT projects managed only by operational people. They cared about the end result, but not the implications in term of IT constraints. In the end, you have a big nightmare.”
In other words: well-intentioned local initiatives, zero standardization. The kind of environment where every plant has its own version of the truth.
So where do you start when the elephant is that big?
“We started with the most painful issue: the end-of-line quality control system. Each plant had its own version. We moved from local executable applications to a web-based, centralized one.”
Then came work instructions, and so on and so on. It was a classic “bite-by-bite” transformation.
How COVID Changed the Game
Like many others, Poclain had big plans for a global MES rollout. And then COVID hit. Budgets froze, priorities shifted, and suddenly the grand plan was off the table.
“We had to rethink everything. How can we do more with less? How can we use what we already have?”
What followed was a shift from “big system thinking” to a more agile, best-of-breed approach.
“I always say it’s not a happy event for everyone, but I thank COVID-19,” he laughs. “It forced us to be creative.”
That creativity led to the Data Hub project: a pragmatic approach to connecting existing systems, automating data collection, and building live dashboards that operators could actually use.
Building a DataOps Mindset
The guiding principle was simple: make data useful, make it live, and make it easy for non-IT users.
“I didn’t want my team to be the bottleneck. The system should be usable by non-IT people.”
That requirement drove their vendor evaluation which eventually led to selecting Litmus.io as their main Data Hub platform.
“Since 2021, we’ve been implementing Litmus as our main data hub. Step by step, we break the silos and build on it.”
But technology was only one part of the story. The harder part was governance and culture.
“It took a lot of time to explain to top management that the Data Hub is just an enabler. It’s not magic. You need something meaningful for the people at the plants on top of it.”
Standardization Without Killing Flexibility
Today, Poclain’s model combines global consistency with local agility.
“We master the data model centrally and duplicate it for each site. Plants can adapt the templates locally by defining their equipments and their mappings, but the core remains the same.”
The result?
Faster rollouts, cleaner data, and dashboards that update automatically without anyone touching Excel.
Rija’s model proves that digital transformation doesn’t have to mean disruption, just the right balance between structure and freedom, one data point at a time.
Interested in knowing more about Litmus? A few months ago we published our 5 Step Playbook for a Painless DataOps Rollout:
And have you already listened to our Industrial DataOps podcast with John Younes?

Stay Tuned for More!
🚀 Join the ITOT.Academy →
Subscribe to our podcast and blog to stay updated on the latest trends in Industrial Data, AI, and IT/OT convergence.
🚀 See you in the next episode!
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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com

📣 A quick reminder before we start: Our next ITOT.Academy kicks off on January 23, and our early bird offer is still available. Do you want to join our third group and learn how to bridge IT and OT? There is no better time than now! 👉 Check the curriculum on ITOT.Academy or in this previous article.

When Anton Melander joined Northvolt in 2018, the company had around 100 employees and zero factories. The goal? Build Europe’s first large-scale lithium-ion battery production from scratch — and do it fast.
“When I joined, there was nothing. I joined the digitalization department, which at the time was me and three others,” Anton told us. “We knew making batteries was fast — really fast — and with tight tolerances. Even microns of misalignment could lead to short circuits. So we wanted to be as data-driven as possible when scaling production.”
From that tiny team, Northvolt’s digitalization function grew to over 140 people, while the company itself ballooned to 5,000 employees. It’s a rare story (and one that ended too soon, after Northvolt’s bankruptcy earlier this year). But in those years, Anton learned what it truly means to build an OT organization from nothing, and why many of those lessons are now shaping his next chapter as a startup founder.
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Greenfield Decisions
Northvolt’s early advantage was also its biggest challenge: a completely blank slate.
“We had to figure out what to build and what to buy,” Anton said. “Off-the-shelf software meant long requirement lists, consultants, and change orders. Building in-house gave us flexibility, but it also meant taking full ownership.”
At the time, OPC UA was only beginning to gain traction. Northvolt pushed its equipment suppliers to provide compliant servers and built its own IoT platform to handle the data. “We provisioned over a thousand gateways,” Anton recalled. “Just setting that up was a project in itself.”
That internal platform became the backbone of production — the foundation of what they later called North Cloud, an internal MES connecting operations, quality, and material flow. “We built a lot ourselves,” Anton said, “but we still leveraged AWS for the cloud, and bought things like ERP and PLM. It was a mix, but a deliberate one.”
From Projects to Products
As production ramped up, the company’s digital organization had to evolve.
“In the beginning, we were completely project-based,” Anton explained. “But as production started, we realized that technology is one thing — it’s only useful if it actually helps people do their job. So we moved from a project-oriented way of working into a product-oriented one.”
That shift (which many IT/OT teams wrestle with) required a mindset change. “Everyone wants their feature,” he said. “The backlog keeps growing forever. But not everything that sounds important actually moves the needle. You need to tie initiatives to measurable results: quality, throughput, yield.”
He laughs looking back. “Sometimes senior stakeholders would say, ‘This is the most important thing,’ and you had to tell them: ‘It’s not going to increase throughput or quality.’ That’s the hard part.”
His other big lesson? Master data.
“You can’t calculate OEE if you don’t know the ideal cycle time. You can’t calculate downtime if you don’t know the planned uptime. It’s easy to draw perfect architecture diagrams, it’s harder to make them work in practice.”
What Comes After Northvolt
Anton left Northvolt in 2023, before its final collapse, but the experience left him with two realizations: first, that building your own tech stack is both empowering and costly and second, that most manufacturers will never have that luxury.
“More than 90 percent of manufacturing companies have fewer than 200 employees,” he said. “They can’t hire 140 people to build their own MES or IoT platform. And yet, they still need data.”
That insight became the starting point for his new company, Ronja, which focuses on helping small and mid-sized manufacturers make sense of the data they already have.
“We’ve spoken to more than 300 manufacturers since we started,” Anton told us. “Almost all of them say the same thing: they have lots of data, but they’re not using it. The problem isn’t collecting data, it’s getting value out of it.”
Ronja’s approach isn’t to replace systems, but to sit on top of what exists, making data accessible to non-technical users. “In most factories,” he said, “data lives in Excel, in emails, in historians. We help people connect it, visualize it, and analyze it faster — without waiting for a two-year MES rollout that eats the entire budget.”
Closing Thoughts
Northvolt’s story is one of ambition and hard-earned lessons: a company that built everything from scratch, scaled fast, and still couldn’t outrun industrial reality. But its alumni, like Anton, carry those lessons forward.
His takeaway applies to anyone working on digital transformation, from startups to global enterprises:
“The closer you are to the shopfloor, the more unique every factory becomes. You can’t standardize everything — but you can make it easier to understand, to learn, and to improve.”
And that, ultimately, is what industrial digitalization has always been about.
Stay Tuned for More!
🚀 Join the ITOT.Academy →
Subscribe to our podcast and blog to stay updated on the latest trends in Industrial Data, AI, and IT/OT convergence.
🚀 See you in the next episode!
Youtube: https://www.youtube.com/@TheITOTInsider Apple Podcasts:
Spotify Podcasts:
Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com