The IT/OT Insider Podcast - Pioneers & Pathfinders

The Forgotten Foundation: PID control and Process Automation with Prof. Margret Bauer

📣 A quick reminder before we start: Our next ITOT.Academy kicks off in May, and our early bird offer is available once more. Want to join our fourth group and learn how to bridge IT and OT? There is no better time than now! 👉 Check the curriculum & enrol via ITOT.Academy [https://itot.academy] 👉 If you’ve been following our blog and podcast, you know we spend most of our time in what we call the IT/OT zone: data platforms, connectivity, governance, AI use cases, and everything in between. We’ve also covered the Purdue model [https://itotinsider.substack.com/p/isa-95-and-the-purdue-model-explained], MES [https://itotinsider.substack.com/p/lets-talk-manufacturing-execution], UNS [https://itotinsider.substack.com/p/new-video-the-unified-namespace-explained], and even Model Predictive Control [https://itotinsider.substack.com/p/lets-talk-about-model-predictive]. However, we rarely talk about what happens at Level 1 and Level 2 — the actual process control layer that keeps plants running. Not the data it produces. Not the dashboards built on top of it. The control itself. So when we had the chance to sit down with Margret Bauer [https://www.linkedin.com/in/margret-bauer-a885618/], Professor of Process Automation at the Hamburg University of Applied Sciences, we jumped at it. Margret is an electrical engineer by training, did her PhD in data analytics on process data back in the early 2000s (before “data analytics” was cool), worked for ABB Corporate Research, and even did early IT/OT integration work — connecting SAP with ABB’s 800xA system back in 2007. (Yes, 2007) PID: The Most Important Algorithm Most Don’t Know About Let’s talk about PID control. Not P&ID (the diagram) — PID, short for proportional, integral, derivative. If you studied engineering, you probably had one half-lecture on it, sandwiched between Kalman filters and Lyapunov functions. Easy to overlook. Except it runs the world. Margret was blunt about this: 99.9% of all rockets that have flown into space run on PID control. All the robots you see online? PID underneath. Every valve opening and closing in a chemical plant, a refinery, a bakery? PID. The concept is elegant: the proportional part looks at the present, the integral part looks at the past, and the derivative part looks at the future. Three aspects of time, one controller. As Margret put it: it has the worst name and the best track record of any control strategy out there. But don’t let the simplicity fool you. In practice, PID is hard to implement well. Valves have physical limits — they can’t open beyond 100% (no matter how politely you ask). They take time to respond. And when you need to coordinate two valves for the same flow — say, one big valve for coarse control and a small valve for fine-tuning — the strategies on top of PID get complex fast. These layered strategies exist across every process plant, and they are the strategies that nobody outside the automation world ever talks about. A Dying Breed Margret posted on LinkedIn that process control engineers are a dying breed. When we asked why, her answer was painfully logical: the automation worked. Companies invested in control systems in the 1970s, 80s, and 90s. Plants got more stable. And then management looked at the 20-person controls department and said: “Why do we still need these people? The process runs fine.” So they cut the teams. One by one, across the industry. And that is a major problem. In industry, many control departments are gone — and with them, the expertise to improve or even maintain automation performance. And in academia, process control is barely taught anymore. There are barely any new process control engineers coming through the pipeline. The academics who still focus on it? A handful worldwide, passionate but outnumbered (and Margret surely is passionate 🙂) The AI Reality Check Willem couldn’t resist: “Margret, of course, I’m going to come in with the solution for all your problems. You need to use AI. It’s going to solve everything.” (We all laughed.) Margret’s response was obviously more measured. One of her master’s students developed a reinforcement learning algorithm for a batch penicillin process that improved throughput by 25%. Genuinely impressive. But it worked because the student had a well-understood simulation model. In the real world? The algorithm wasn’t scalable, wasn’t repeatable, and wouldn’t transfer to another process. This ties straight into something we’ve been discussing a lot recently on this blog: the physical twin problem [https://itotinsider.substack.com/p/industrial-ai-unpacked-introducing]. AI models need to understand the underlying physics, the process behaviour, the control strategies. Without that, you’re optimising in a vacuum. David’s own experience with nonlinear MPC during his master’s thesis confirmed the same thing — beautiful results on simulated data, useless on real plant data. The takeaway isn’t that AI can’t help. It’s that AI without process knowledge is just maths looking for a purpose. The Operator Paradox There’s another angle Margret brought up that resonated with us: the better your automation, the more bored your operators become. One of her students — a former operator — said she used to bring a book to her shift. Press the button, sit down, read for eight hours, hand over to the next shift. That’s great from a stability standpoint. But it creates a dangerous gap. When something does go wrong — and it always does eventually — operators haven’t seen enough upsets to know how to respond. The more you automate, the less exposed your operators are to disturbances, and the harder it becomes to train them for the exceptions. And you can’t just “turn off the MPC layer to make things interesting again,” as David pointed out. So the industry adds another layer — operator training simulators, essentially flight simulators for plant operations. Layer upon layer upon layer. Margret’s view? We’ll never fully automate everything. Every process is different, every plant is an individual. We’ll always need people. The question is how we keep them engaged, trained, and ready for the moments that matter. Why This Matters for the IT/OT World If you’re reading this blog, chances are you’re working on data platforms, digital twins, AI use cases, or integration architectures. All of that is important. But it all sits on top of a foundation that most of us take for granted. Process automation isn’t a solved problem. It’s an under-invested, under-documented, under-appreciated layer that directly determines the quality of the data we work with, the stability of the processes we try to optimise, and the feasibility of the AI models we try to deploy. If the foundation crumbles, nothing above it works. So next time you’re debugging a data quality issue, or wondering why your AI model produces nonsense, or trying to understand why a sensor reading oscillates when it shouldn’t — maybe the answer isn’t in your data platform. Maybe it’s one layer below. Find Margret on LinkedIn: https://www.linkedin.com/in/margret-bauer-a885618/ [https://www.linkedin.com/in/margret-bauer-a885618/] Book ‘Process Control in Practice’ mentioned during the podcast: https://www.amazon.de/Process-Control-Practice-Gruyter-Textbook/dp/3111103722 [https://www.amazon.de/Process-Control-Practice-Gruyter-Textbook/dp/3111103722] 📣 Our next ITOT.Academy kicks off in May, and our early bird offer is available once more. Want to join our fourth group and learn how to bridge IT and OT? There is no better time than now! 👉 Check the curriculum & enrol via ITOT.Academy [https://itot.academy] 👉 Stay Tuned for More! 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 [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 [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

OT Data Governance with Wybren van der Meer

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/ [https://www.linkedin.com/in/wvandermeer/]  More on the Unified Namespace: https://www.youtube.com/watch?v=d1QeZWb6rt0 [https://www.youtube.com/watch?v=d1QeZWb6rt0] More on the Industrial Data Platform: https://www.youtube.com/watch?v=mdtY2Ks8F6M [https://www.youtube.com/watch?v=mdtY2Ks8F6M]  Learn everything about IT/OT Cooperation, Industrial DataOps and more: https://itot.academy [https://itot.academy]  More about The IT/OT Insider: https://itotinsider.com/ [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 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 [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Data as the Common Thread: Process Safety, Metrics, and Career Lessons with Kris Doering

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 [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 [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 [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 [https://itotinsider.substack.com/i/157198261/sooner-safer-happier]. Stay Tuned for More! 🚀 Join the ITOT.Academy ( [https://itot.academy]May [https://itot.academy] and [https://itot.academy]September [https://itot.academy] Early birds now available) → [https://itot.academy] Subscribe to our podcast and blog to stay updated on the latest trends in Industrial Data, AI, and IT/OT convergence. Youtube: https://www.youtube.com/@TheITOTInsider [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 [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

When Physics Meets AI: A Conversation with Dan Jeavons

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 [https://www.linkedin.com/in/dan-jeavons-a43b3b2/], president of Applied Computing [https://appliedcomputing.com/], 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 → [https://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 [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 [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Overcoming the Impossible: DataOps at Poclain Hydraulics with Rija Rakotoarisoa

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 [https://www.linkedin.com/in/rijarakotoarisoa/], 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 [https://litmus.io/] as their main Data Hub platform. “Since 2021, we’ve been implementing Litmus [https://litmus.io/] 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 [https://litmus.io]? 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 → [https://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 [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 [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]