The IT/OT Insider Podcast with David and Willem

In this episode of the IT/OT Insider Podcast, we welcome someone who doesn’t come from cloud platforms, data infrastructure, or connectivity layers. Instead, he brings something equally vital: operational wisdom.

Raf Swinnen has spent his career inside factories. From Procter & Gamble to Kellogg's, and later Danone, Raf worked at the intersection of operations and transformation, guiding teams through continuous improvement and later, digital initiatives.

What makes his perspective especially valuable? It’s grounded in Lean thinking. Not as a buzzword, but as a real discipline. One that requires a sharp understanding of processes, a respect for people on the floor, and a strong filter for what actually adds value.

From Line Leader to Digital Change Agent

Raf didn’t start in digital. He started on the floor: managing lines, people, safety, and performance. That experience shaped how he sees digital transformation today: as something that should support operations, not get in the way of them.

At Danone, he led digital initiatives at the Rotselaar site (Belgium). The job wasn’t to implement more dashboards. It was to help teams use data to drive better decisions, without losing sight of the fundamentals.

“Tech to the Back” — What Digital Should Learn from Lean

One of the most powerful takeaways from this episode is Raf’s principle of “Tech to the back.”

“Digital solutions should not be front and center. People and processes should be. Tech should follow.”

This is a strong antidote to the over-designed, solution-first approaches that often flood the industrial space. According to Raf, the biggest risk in digital projects isn’t the technology — it’s losing the problem along the way.

Three C’s: Connect, Collaborate, and Coherence

As part of his work with leadership teams, Raf often introduces what he calls the 3 C’s:

* Clarity – Where are we going, and why?

* Consistency – Are we reinforcing the same messages and systems?

* Coherence – Do our tools, apps, and data work together?

These are not slogans, they are essential behaviors for any transformation to stick. They also align closely with how we designed the ITOT.Academy, where cross-role learning and shared frameworks are front and center.

One of Raf’s biggest contributions came through how he structured teams. In a newly created role as Digital Program Manager, he pulled in both IT and OT voices and even shifted reporting lines to foster true collaboration.

He didn’t look for tech wizards. He looked for people with enthusiasm. People who wanted to make a difference. These became his digital ambassadors, key voices from every shift, every team.

“When the night shift speaks up, you listen. They see the edge cases nobody else does.”

Case Examples: Real Change Starts Small

Raf shared stories from his time at Danone, Kellogg’s, and P&G, where transformation didn’t come from big declarations — but from small, disciplined steps.

At one plant, it was about helping teams make better use of their shift handovers.At another, it meant cleaning up data before launching another round of training.At Danone, the challenge was scaling good ideas without flattening local ownership.

“Digital without context is noise. The real challenge is creating relevance at the point of use.”

Digital with Discipline

Raf’s story is a reminder that digital transformation doesn’t start with technology, it starts with understanding the process. Listening to the people who run it, and designing with clarity and purpose. Whether it's Lean principles, cultural alignment, or simply asking better questions, his approach keeps the focus where it matters: on solving real problems in practical ways.

In a time when industrial tech is advancing fast and buzzwords multiply by the day, it’s refreshing to hear someone say: let’s not forget why we’re doing this in the first place.

If you’re working in digital, operations, or somewhere in between, this episode is a pause-and-reflect moment.

And maybe also a nudge: to push tech to the back, and put people and purpose out front.

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.

📣 Quick note before we dive into all things open source:

In our last episode, we announced the launch of the ITOT.Academy: a live on-line learning experience for professionals navigating the complex world of IT/OT collaboration.

Our early bird seats are filling up fast. If you’re serious about gaining practical skills (not just theory), now’s the time to secure your spot. Don’t wait too long, the first cohorts start on August 29 and September 5 (each cohort consists of six 2 hour sessions and you receive all recordings).

👉 Full training program and registration via ITOT.Academy

In this episode of the IT/OT Insider Podcast, we sit down with Alexander Krüger, co-founder and CEO of United Manufacturing Hub (UMH), to talk about something that’s both old and revolutionary in the industrial world: open source software.

This isn’t about hobby projects or side experiments. It’s about why open source is playing an increasingly important role in how factories move data, scale operations, and reduce vendor lock-in. Alexander brings both a technical and business perspective and shares what happens when a mechanical engineer dives deep into the world of cloud-native data infrastructure.

Not all Open Source is created equal

Most industrial companies still equate reliability with paying a vendor and signing a service-level agreement. But Alexander challenges that mindset. His team originally built UMH because they were frustrated with how hard it was to try, test, and scale traditional industrial software.

“We just wanted to get data from A to B in a factory, but realized that problem isn’t really solved yet. So we made it open source.”

Alexander is quick to point out that choosing open source doesn’t automatically mean less risk, but it does mean different trade-offs. Key factors include:

* Licensing clarity

* Community health (Is it maintained? Is it active?)

* Governance (Who controls the roadmap? What happens if they change direction?)

He even brings up the infamous example of vendors repackaging tools like Node-RED under different names, then charging for them without giving proper credit (or worse, shipping outdated versions).

“If you’re already bundling open source into your software, why not be honest about it?”

What about reliability?

If you’re an OT leader, you might still worry: who do I call at 2 a.m. when something breaks?

Alexander’s answer: you should be asking that question about any software, open or proprietary. Because often, what fails isn’t the software itself, it’s the integrations someone built in a rush, or the one engineer who knew how things worked and then left the company.

With open source, there’s at least transparency, control, and the ability to maintain continuity. You’re not locked out of your own systems.

The Human Side: The rise of the hybrid engineer

One of the most interesting parts of the conversation was about who will make this all work. Alexander sees a new kind of engineer emerging: someone with a background in OT, but who enjoys learning IT concepts, tinkering with Docker, and embracing DevOps practices.

“We’re looking for people who used to live in TIA Portal but now run state of the art home automation in their free time.”

This isn’t about turning everyone into a software developer. But it is about building a culture where people are open to learning from both sides and using modern ways of working and new tools to solve old problems.

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 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.

Discover the program and claim your seat here: https://itot.academy 

🎙️ In this special episode of the IT/OT Insider Podcast, David and Willem officially announce the launch of the ITOT.Academy!

After years of conversations with IT/OT professionals, consultants, and technology vendors, one thing became clear: there’s a huge need for practical, vendor-neutral education to help people work together across IT and OT boundaries.

The ITOT.Academy is designed to fill that gap.

What you’ll learn in this episode:Why we created the AcademyWho it's for: OT teams, IT teams, consultants, vendorsThe structure of the program: short, live, interactive sessionsWhy it's not about convergence but collaborationWhen the first groups will startHow to sign up and join the first cohorts🚀 Learn more and sign up at https://itot.academy

🎧 Subscribe for more honest conversations on bridging IT and OT.

Chapters

00:00 Introduction to ITOT Academy

01:38 Feedback from Subscribers

03:47 Target Audience for Training

07:24 Training Format and Structure

11:19 Core Concepts of the Training

13:32 Interactive Sessions and Wrap-Up

14:37 Launch Details and Closing Remarks

It is episode 31 and we’re finally tackling a topic that somehow hadn’t made the spotlight yet: IoT. And we couldn’t have asked for two better guests to help us dive into it: Olivier Bloch and Ryan Kershaw.

This is not your usual shiny, buzzword-heavy conversation about the Internet of Things. Olivier and Ryan bring decades of hands-on experience from both sides of the IT/OT divide: Olivier from embedded systems, developer tooling, and cloud platforms, Ryan from the shop floor, instrumentation, and operational systems. Together, they’re building bridges where others see walls.

IoT 101

Olivier kicks things off with a useful reset:

"IoT is anything that has compute and isn’t a traditional computer. But more importantly, it’s the layer that lets these devices contribute to a bigger system: by sharing data, receiving commands, and acting in context."

Olivier has seen IoT evolve from standalone embedded devices to edge-connected machines, then cloud-managed fleets, and now towards context-aware, autonomous systems that require real-time decision-making.

Ryan, meanwhile, brings us back to basics:

"When I started, a pH sensor gave you one number. Now, it gives you twelve: pH, temperature, calibration life, glass resistance... The challenge isn’t getting the data. It’s knowing what to do with it."

Infrastructure Convergence: The Myth of the One-Size-Fits-All Platform

We asked the obvious question: after all these years, why hasn’t “one platform to rule them all” emerged for IoT?

Olivier’s take is straightforward:

"All the LEGO bricks are out there. The hard part is assembling them for your specific need. Most platforms try to do too much or don’t understand the OT context."

You can connect anything these days. The real question is: should you? Start small, solve a problem, and build trust from there.

Why Firewalls are no longer enough

Another highlight: their views on security and zero trust in industrial environments.

Olivier and Ryan both agree: the old-school "big fat firewall" between IT and OT isn’t enough.

"You’re not just defending a perimeter anymore. You need to assume compromise and secure each device, user, and transaction individually."

So what is Zero Trust, exactly? It’s a cybersecurity model that assumes no device, user, or system should be automatically trusted, whether it’s inside or outside the network perimeter. Instead of relying on a single barrier like a firewall, Zero Trust requires continuous verification of every request, with fine-grained access control, identity validation, and least-privilege permissions. It’s a mindset shift: never trust, always verify.

They also emphasize that zero trust doesn’t mean "connect everything." Sometimes the best security strategy is to not connect at all, or to use non-intrusive sensors instead of modifying legacy equipment.

Brownfield vs. Greenfield: Two different journeys

When it comes to industrial IoT, where you start has everything to do with what you can do.

Greenfield projects, like new plants or production lines, offer a clean slate. You can design the network architecture from the ground up, choose modern protocols like MQTT, and enforce consistent naming and data modeling across all assets. This kind of environment makes it much easier to build a scalable, reliable IoT system with fewer compromises.

Brownfield environments are more common and significantly more complex. These sites are full of legacy PLCs, outdated SCADA systems, and equipment that was never meant to connect to the internet. The challenge is not just technical. It's also cultural, operational, and deeply embedded in the way people work.

"In brownfield, you can’t rip and replace. You have to layer on carefully, respecting what works while slowly introducing what’s new," said Ryan.

Olivier added that in either case, the mistake is the same: moving too fast without thinking ahead.

"The mistake people make in brownfield is to start too scrappy. It’s tempting to just hack something together. But you’ll regret it later when you need to scale or secure it."

Their advice is simple:

Even if you're solving one problem, design like you will solve five. That means using structured data models, modular components, and interfaces that can evolve.

Final Thoughts

This episode was a first deep dive into real-world IoT—not just the buzzwords, but the architecture, trade-offs, and decision-making behind building modern industrial systems.

From embedded beginnings to UNS ambitions, Thing-Zero is showing that the future of IoT isn’t about more tech. It’s about making better choices, backed by cross-disciplinary teams who understand both shop floor realities and enterprise demands.

To learn more, visit thing-zero.com and check out Olivier’s YouTube channel “The IoT Show” for insightful and developer-focused content.

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 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.

Today, we have the pleasure of speaking with Nikki Gonzales, Director of Business Development at Weintek USA, co-founder of the Automation Ladies podcast, and co-organizer of OT SCADA CON—a conference focused on the gritty, real-world challenges of industrial automation.

Unlike many of our guests who often come from cloud-first, data-driven digitalization backgrounds, Nikki brings a refreshing and much-needed OT floor-level perspective. Her world is HMI screens, SCADA systems, manufacturers, machine builders, and the hard truths about where industry transformation actually stands today.

What’s an HMI and Why Does It Matter?

In Nikki’s words, an HMI is:

"The bridge between the operator, the machine, and the greater plant network."

It’s often misunderstood as just a touchscreen replacement for buttons—but Nikki highlights that a modern HMI can do much more:

* Act as a gateway between isolated machines and plant-level networks.

* Enable remote access, alarm management, and contextual data sharing.

* Help standardize connectivity in mixed-vendor environments.

The HMI is often the first step in connecting legacy equipment to broader digital initiatives.

Industry 3.0 vs. Industry 4.0: Ground Reality Check

While the industry buzzes with Industry 4.0 (and 5.0 🙃) concepts, Nikki’s view from the field is sobering:

"Most small manufacturers are still living in Industry 3.0—or earlier. They have mixed equipment, proprietary protocols, and minimal digitalization."

For the small manufacturers Nikki works with, transformation isn't about launching huge digital projects. It’s about taking incremental steps:

* Upgrading a handful of sensors.

* Introducing remote monitoring.

* Standardizing alarm management.

* Gradually building operational visibility.

"Transformation for small companies isn’t about fancy AI. It’s about survival—staying competitive, keeping workers, and staying in business."

With labor shortages, supply chain pressures, and rising cybersecurity threats, smaller manufacturers must adapt—but they have to do it in a way that is affordable, modular, and low-risk.

UNS, SCADA, and the State of Connectivity

Nikki also touched on how concepts like UNS (Unified Namespace) are being discussed:

"Everyone talks about UNS and cloud-first strategies. But in reality, most plants still have islands of automation. They have to bridge old PLCs, proprietary protocols, and aging SCADA systems first."

While UNS represents a desirable goal—a real-time, unified data model accessible across the enterprise—many manufacturers are years (or even decades) away from making that a reality without significant groundwork first.

In this world, HMI upgrades, standardized communication protocols (like MQTT), and targeted SCADA modernization become the critical building blocks.

The Human Challenge: Culture and Workforce

Beyond the technology, Nikki highlighted the human side of transformation:

* Younger generations aren't attracted to repetitive, low-tech manufacturing jobs.

* Manual, isolated processes make hiring and retention even harder.

* Manufacturers must rethink how technology supports not just efficiency, but employee satisfaction.

The future of manufacturing depends not just on smarter machines—but on designing operations that attract and empower the next generation of workers.

Organizing a Conference from Scratch: OT SCADA CON

Before wrapping up, we asked Nikki about organizing OT SCADA CON.

"You need a little naivety, a lot of persistence, and the right partners. We jumped first, then figured out how to build the plane on the way down."

OT SCADA CON is designed by practitioners for practitioners—short technical sessions, no vendor pitches, no buzzword bingo. Just real, practical advice for the engineers, integrators, and plant technicians who make industrial operations work.

Final Thoughts

In a world obsessed with the future, Nikki reminds us:

You can't build Industry 4.0 without first fixing Industry 3.0.

And fixing it starts with respecting the complexity, valuing the small steps, and supporting the people on the ground who keep manufacturing running.

If you want to learn more about Nikki’s work, visit automationladies.io and check out OT SCADA CON, taking place July 23–25, 2025.

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 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.

Welcome to another episode of the IT/OT Insider Podcast. Today, we’re diving into the world of Manufacturing Execution Systems (MES) and Manufacturing Operations Management (MOM) with Matt Barber, VP & GM MES at Infor. With over 15 years of experience, Matt has helped companies worldwide implement MES solutions, and he’s now on a mission to educate the world about MES through his website, MESMatters.com .

MES is a topic that sparks a lot of debate, confusion, and, in many cases, hesitation. Where does it fit in a manufacturing tech stack? How does it relate to ERP, Planning Systems, Quality Systems, or industrial data platforms? And what’s the real difference between MES and MOM?

These are exactly the questions we’re tackling today.

Thanks for reading The IT/OT Insider! Subscribe for free to receive new posts and support our work.

MES vs. MOM: What’s the Difference?

Matt opens the discussion by addressing one of the misconceptions in the industry-what actually defines an MES, and how it differs from MOM.

"An MES is a specific type of application that focuses on production-related activities-starting and stopping production orders, tracking downtime, recording scrap, and calculating OEE. That’s the core of MES."

But MOM is broader. It extends beyond production into quality management, inventory tracking, and maintenance. MOM isn’t a single application but rather a framework that connects multiple operational functions.

Many MES vendors include some MOM capabilities, but few solutions cover all aspects of production, quality, inventory, and maintenance in one system. That’s why companies need to carefully evaluate what they need when selecting a solution.

How Do Companies Start with MES?

Not every company wakes up one day and decides, “We need MES.” The journey often starts with a single pain point-a need for OEE tracking, real-time visibility, or better quality control.

Matt outlines two main approaches:

* Step-by-step approach

* Companies start with a single use case, such as tracking downtime and production efficiency.

* Once they see value, they expand into areas like quality control, inventory tracking, or maintenance scheduling.

* This approach minimizes risk and allows for quick wins.

* Enterprise-wide standardization

* Larger companies often take a broader approach, aiming to standardize MES across all sites.

* The goal is to ensure consistent processes, better data integration, and a unified system for all operators.

* While it requires more planning and investment, it creates a cohesive manufacturing strategy.

Both approaches are valid, but Matt emphasizes that even if companies start small, they should have a long-term vision of how MES will fit into their broader Industry 4.0 strategy.

The Role of OEE in MES

OEE (Overall Equipment Effectiveness) is one of the most common starting points for MES discussions. It measures how much good production output a company achieves compared to its theoretical maximum.

The three key factors:

* Availability – How much time machines were available for production.

* Performance – How efficiently the machines ran during that time.

* Quality – How much of the output met quality standards.

"You don’t necessarily need an MES to track OEE. Some companies do it in spreadsheets or standalone IoT platforms. But if you want real-time OEE tracking that integrates with production orders, material usage, and quality data, MES is the natural solution."

People and Process: The Hardest Part of MES Implementation

One of the biggest challenges in MES projects isn’t the technology-it’s people and process change.

Matt shares a common issue:

"Operators often have their own way of doing things. They know how to work around inefficiencies. But when an MES system is introduced, it enforces a standardized way of working, and that’s where resistance can come in."

To make MES adoption successful, companies must:

* Get leadership buy-in – A clear vision from the top ensures the project gets the necessary resources and support.

* Engage operators early – Including shop floor workers in the process design increases adoption and usability.

* Define clear roles – Having global MES champions and local site super-users ensures both standardization and flexibility.

"You can have the best MES system in the world, but if no one uses it, it’s worthless."

How the MES Market is Changing

MES has been around for decades, but the industry is evolving rapidly. Matt highlights three major trends:

* The rise of configurable MES

* Historically, MES projects required custom coding and long implementation times.

* Now, companies like Infor are offering out-of-the-box, configurable MES platforms that can be set up in days instead of months.

* Companies that offer configurable OTB applications (like Infor) are able to offer quick prototyping for manufacturing processes, ensuring customers benefit from agility and quick value realisation.

* The split between cloud-based MES and on-premise solutions

* Many legacy MES systems were designed to run on-premise with deep integrations to shop floor equipment.

* However, cloud-based MES is growing, especially in multi-site enterprises that need centralized management and analytics.

* Matt recognises the importance of cloud based applications, but highlights that there will always be at least a small on-premise part of the architecture for connecting to machines and other shopfloor equipment.

* MES vs. the rise of “build-it-yourself” platforms

* Some smaller manufacturers opt for the “do-it-yourself” approach, creating their own MES-Light applications by layering in various technologies and software platforms.

* This trend is more common in smaller manufacturers that need flexibility and are comfortable developing their own industrial applications.

* However, for enterprise-wide standardization, an OTB configurable MES platform provides the best scalability and consistency, and the most advanced platforms allow end-users to configure it themselves through master data, reports, and dashboards.

MES and Industrial Data Platforms

A big topic in manufacturing today is the role of data platforms. Should MES be the central hub for all manufacturing data, or should it feed into an enterprise-wide data lake?

Matt explains the shift:

"Historically, MES data was stored inside MES and maybe shared with ERP. But now, with the rise of AI and advanced analytics, manufacturers want all their industrial data in one place, accessible for enterprise-wide insights."

This has led to two key changes:

* MES systems are increasingly required to push data into (industrial) data platforms.

* Companies are focusing on data contextualization, ensuring that production data, quality data, and maintenance data are all aligned for deeper analysis.

"MES is still critical, but it’s no longer just an execution layer-it’s a key source of contextualized data for AI and machine learning."

Where to Start with MES

For companies considering MES, Matt offers some practical advice:

* Understand your industry needs – Different MES solutions are better suited for different industries (food & beverage, automotive, pharma, etc.).

* Start with a clear business case – Whether it’s reducing downtime, improving quality, or optimizing material usage, have a clear goal.

* Choose between out-of-the-box vs. build-your-own – Large enterprises may benefit from standardized MES, while smaller companies might prefer DIY industrial platforms.

* Don’t ignore change management – Successful MES projects require strong collaboration between IT, OT, and shop floor operators.

"It’s hard. But it’s worth it."

Final Thoughts

MES is evolving faster than ever, blending traditional execution functions with modern cloud analytics. Whether companies take a step-by-step or enterprise-wide approach, MES remains a critical piece of the smart manufacturing puzzle.

For more MES insights, check out mesmatters.com or Matt’s LinkedIn page, and don’t forget to subscribe to IT/OT Insider for the latest discussions on bridging IT and OT.

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 Apple Podcasts:

Spotify Podcasts:

In this episode of the IT/OT Insider Podcast, where we’re taking a short detour from our usual deep dives into industrial things to explore something broader-but equally vital: how enterprises evolve.

We’re joined by Stephen Fishman and Matt McLarty, authors of the book Unbundling the Enterprise, published by IT Revolution. Stephen is North America Field CTO at Boomi, and Matt is the company’s Global CTO. But more importantly for this conversation-they’re long-time collaborators with a shared passion for modularity, APIs, and systems thinking.

We’ll talk about the power of preparation over prediction, about how modular systems and composable strategies can future-proof organizations, and-most unexpectedly-how happy accidents (yes, “OOOPs”) can unlock unexpected success.

Thanks for reading The IT/OT Insider! Subscribe for free to receive new posts and support our work.

From Creative Writing to Enterprise Architecture

Stephen and Matt first connected over a decade ago, when Stephen was leading app development at Cox Automotive and Matt was heading up the API Academy at CA Technologies. Their collaboration grew from a shared curiosity: why were APIs making some companies wildly successful, and why did that success often seem... unplanned?

They didn’t want to write yet another how-to book on APIs. Instead, they wanted to tell the bigger story-about why companies who invested in modularity were able to respond faster, seize opportunities more easily, and unlock new business models.

“We wanted to bridge the gap between architects and the business. Help tech teams articulate why they want to build things in a modular way-and help business folks understand the financial value behind those decisions.” – Stephen Fishman

OOOPs: The Power of Happy Accidents

One of the big themes in their book is what the authors call OOOPs-not a typo, but an acronym.

“Google Maps is the classic story,” Stephen explains. “People started scraping the APIs and using them in ways Google never planned-until they turned it into a massive business. That was a happy accident. And it happened again and again.”

So they gave those happy accidents a structure-Optionality, Opportunism, and Optimization.

* Optionality: Modular systems open the door to future opportunities you can’t yet predict.

* Opportunism: You need ways to identify where to unbundle or where to apply APIs first.

* Optimization: Continuously measuring and refining based on real usage and feedback.

This framework makes the case that modularity isn’t just a technical preference-it’s a business strategy.

Read more about OOOps in this article.

S-Curves, Options, and Becoming the House

Another concept that runs through the book is the S-curve of growth-the idea that all successful innovations follow a familiar pattern: slow start, rapid rise, plateau, and eventual decline.

Most companies ride that first curve too long, betting too heavily on what worked yesterday. The challenge is recognizing when you’ve peaked-and investing in what comes next.

“Most people don’t know where they are on the S-curve,” says Stephen. “They think they’re still climbing, but they’re really on the plateau.”

That’s where optionality comes in again: the ability to explore multiple futures at low cost, hedging your bets without breaking the bank. They borrow the idea of “convex tinkering”: placing lots of small, low-cost bets with the potential for high upside.

“Casinos don’t gamble,” Stephen says. “They set the rules. They optimize for asymmetric value. That’s what this book is trying to teach organizations-how to become the house.”

We also wrote about the importance of having cost effective ways to work with data in this previous post:

Unbundling is Not Just for Big Tech

You might think this is a book for Google, Amazon, or SaaS unicorns-but the lessons apply to every enterprise. Even in manufacturing.

“The automotive world has always understood modularity,” Stephen says. “Platforms existed in car design before they existed in tech. When you separate chassis from body and engine, you gain flexibility and efficiency.”

And the same applies in IT and OT.

* Building platforms of reusable APIs and services

* Designing products and processes with change in mind

* Investing in capabilities close to revenue, not just internal shared services

Even internal IT teams benefit from this mindset. Once a solution is decontextualized and reusable, it can scale across departments and generate asymmetric value internally-without needing to sell to the outside world.

All Organization Designs Suck (and That’s Okay)

A memorable quote in the book comes from an interview with David Rice (SVP Product and Engineering at Cox Automotive):

“All organization designs suck”

It’s a reminder that there’s no perfect org chart, no flawless model. Instead, success comes from designing your systems, your teams, and your investments with awareness of their limits-and building flexibility around them.

“APIs aren’t a silver bullet. Neither is GenAI. But if you design your systems, teams, and investments around modularity and resilience, you’re better prepared for whatever future emerges.”

We highly recommend the book Team Topologies as further read on this topic.

Final Thoughts

Unbundling the Enterprise is not a technical manual. It’s a mindset. A playbook for organizations that want to survive disruption, scale intelligently, and embrace change-without betting everything on a single future.

The ideas in this book are especially relevant for those working on digital transformation in complex industries. It’s not always about moving fast-it’s about moving smart, building for change, and staying ready.

You can find the book on IT Revolution or wherever great tech books are sold. And be sure to check out their companion article on OOOPs on the IT Revolution blog.

Until next time and stay modular! 🙂

Want More Conversations Like This?

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:

Welcome to another episode of the IT/OT Insider Podcast. Today, we’re diving into visibility, traceability, and real-time analytics with Tim Butler, CEO and founder of Tego.

For the last 20 years, Tego has been specializing in tracking and managing critical assets in industries like aerospace, pharmaceuticals, and energy. The company designed the world’s first rugged, high-memory passive UHF RFID chip, helping companies like Airbus and Boeing digitize lifecycle maintenance on their aircraft.

It’s a fascinating topic—how do you keep track of assets that move across the world every day? How do you embed intelligence directly into physical components? How does all of this connect to the broader challenge of IT and OT convergence? And… how do you create a unified view that connects people, parts, and processes to business outcomes?

Let’s dive in!

Thanks for reading The IT/OT Insider! Subscribe for free to receive new posts and support our work.

From Serial Entrepreneur to Asset Intelligence

Tim’s journey into asset intelligence started 20 years ago, when he saw a major opportunity in industrial RFID technology.

"At the time, RFID chips had only 96 or 128 bits of storage. That was enough for a serial number, but not much else. We set out to design a chip that could hold thousands of times more memory—and that completely changed the game."

That chip became the foundation for Tego’s work in aerospace.

* Boeing and Airbus needed a better way to track assets on planes.

* Maintenance logs and compliance records needed to (virtually) move with the asset itself.

* Standard RFID solutions didn’t have enough memory or durability to survive extreme conditions.

By designing high-memory RFID chips, Tego helped digitize aircraft maintenance and inventory management. They co-authored the ATA Spec 2000 Chapter 9-5 standards that are now widely used in aerospace.

"The challenge was clear—planes fly all over the world, so the data needed to travel with them. We had to embed intelligence directly into the assets themselves."

A Real-World Use Case: Tracking Aircraft Components with RFID

One of the best examples of Tego’s impact is in the aerospace industry.

The Challenge:

* Aircraft components need regular maintenance and compliance tracking.

* Traditional tracking methods relied on centralized databases, which weren’t always accessible.

* When a plane lands, maintenance teams need instant access to accurate, up-to-date records.

The Solution:

* Every critical component (seats, life vests, oxygen generators, galley equipment, etc.) is tagged with a high-memory RFID chip (yes, also the one underneath your next airplane seat probably has one 🙂).

* When a technician scans a tag, they instantly access the asset’s history.

The Impact:

* Reduced maintenance delays—Technicians no longer have to search for data across multiple systems.

* Improved traceability—Every asset has a digital history that travels with it.

* Compliance enforcement—Airlines can quickly verify whether components meet regulatory requirements.

"This isn’t just about making inventory tracking easier. It’s about ensuring safety, reducing downtime, and making compliance effortless."

The IT vs. OT Divide in Aerospace

A major theme of our podcast is the convergence of IT and OT—and in aerospace, that divide is particularly pronounced.

Tim breaks it down:

* IT teams manage enterprise data—ERP systems, databases, and security.

* OT teams manage physical assets—maintenance operations, plant floors, and repair workflows.

* Both need access to the same data, but they use it differently.

"IT thinks in terms of databases and networks. OT thinks in terms of real-world processes. The goal isn’t just connecting IT and OT—it’s making sure they both get the data they need in a usable way."

The Future of AI and Asset Intelligence

With all the buzz around AI and Large Language Models (LLMs), we asked Tim how these technologies are impacting industrial asset intelligence.

His take? AI is only as good as the data feeding it.

"If you don’t have structured, reliable data, AI can’t do much for you. That’s why asset intelligence matters—it gives AI the high-quality data it needs to make meaningful predictions."

Some of the key trends he sees:

* AI-powered maintenance recommendations—Analyzing historical asset data to predict failures before they happen.

* Automated compliance checks—Using AI to validate and flag compliance issues before inspections.

* Smart inventory optimization—Ensuring that spare parts are always available where they’re needed most.

But the biggest challenge? Data consistency.

"AI works best when it has standardized, structured data. That’s why using industry standards—like ATA Spec 2000 for aerospace—is so important."

Final Thoughts

Industrial asset intelligence is evolving rapidly, and Tego is leading the way in making assets smarter, more traceable, and more autonomous.

From tracking aircraft components to ensuring regulatory compliance in pharma, Tego’s technology blends physical and digital worlds, making it easier for companies to manage assets at a global scale.

Together with Tego, businesses create a single source of truth for people, processes, and parts that empowers operations with the vision to move forward.

If you’re interested in learning more about Tego and their approach to asset intelligence, visit www.tegoinc.com.

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 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.

Welcome to the final episode of our special Industrial DataOps podcast series. And what better way to close out the series than with Dominik Obermaier, CEO and co-founder of HiveMQ—one of the most recognized names when it comes to MQTT and Unified Namespace (UNS).

Dominik has been at the heart of the MQTT story from the very beginning—contributing to the specification, building the company from the ground up, and helping some of the world’s largest manufacturers, energy providers, and logistics companies reimagine how they move and use industrial data.

Every Company is Becoming an IoT Company

Dominik opened with a striking analogy:

"Just like every company became a computer company in the ‘80s and an internet company in the ‘90s, we believe every company is becoming an IoT company."

And that belief underpins HiveMQ’s mission—to build the digital backbone for the Internet of Things, connecting physical assets to digital applications across the enterprise.

Subscribe for free to receive new posts and support our work.

Today, HiveMQ is used by companies like BMW, Mercedes-Benz, and Lilly to enable real-time data exchange from edge to cloud, using open standards that ensure long-term flexibility and interoperability.

What is MQTT?

For those new to MQTT, Dominik explains what it is: a lightweight, open protocol built for real-time, scalable, and decoupled communication.

Originally developed in the late 1990s for oil pipeline monitoring, MQTT was designed to minimize bandwidth, maximize reliability, and function in unstable network conditions.

It uses a publish-subscribe pattern, allowing producers and consumers of data to remain decoupled and highly scalable—ideal for IoT and OT environments, where devices range from PLCs to cloud applications.

"HTTP works for the internet of humans. MQTT is the protocol for the internet of things."

The real breakthrough came when MQTT became an open standard. HiveMQ has been a champion of MQTT ever since—helping manufacturers escape vendor lock-in and build interoperable data ecosystems.

From Broker to Backbone: Mapping HiveMQ to the Capability Model

HiveMQ is often described as an MQTT broker, but as Dominik made clear, it's far more than that. Let’s map their offerings to our Industrial DataOps Capability Map:

Connectivity & Edge Ingest →

* HiveMQ Edge: A free, open-source gateway to connect to OPC UA, Modbus, BACnet, and more.

* Converts proprietary protocols into MQTT, making data accessible and reusable.

Data Transport & Integration →

* HiveMQ Broker: The core engine that enables highly reliable, real-time data movement across millions of devices.

* Scales from single factories to hundreds of millions of data tags.

Contextualization & Governance →

* HiveMQ Data Hub and Pulse: Tools for data quality, permissions, history, and contextual metadata.

* Pulse enables distributed intelligence and manages the Unified Namespace across global sites.

UNS Management & Visualization →

* HiveMQ Pulse is a true UNS solution that provides structure, data models, and insights without relying on centralized historians.

* Allows tracing of process changes, root cause analysis, and real-time decision support.

Building the Foundation for Real-Time Enterprise Data

Few topics have gained as much traction recently as UNS (Unified Namespace). But as Dominik points out, UNS is not a product—it’s a pattern. And not all implementations are created equal.

"Some people claim a data lake is a UNS. Others say it’s OPC UA. It’s not. UNS is about having a shared, real-time data structure that’s accessible across the enterprise."

HiveMQ Pulse provides a managed, governed, and contextualized UNS, allowing companies to:

* Map their assets and processes into a structured namespace.

* Apply insights and rules at the edge—without waiting for data to reach the cloud.

* Retain historical context while staying close to real-time operations.

"A good data model will solve problems before you even need AI. You don’t need fancy tech—you need structured data and the ability to ask the right questions."

Fix the Org Before the Tech

One of the most important takeaways from this conversation was organizational readiness. Dominik was clear:

"You can’t fix an organizational problem with technology."

Successful projects often depend on having:

* A digital transformation bridge team between IT and OT.

* Clear ownership and budget—often driven by a C-level mandate.

* A shared vocabulary, so teams can align on definitions, expectations, and outcomes.

To help customers succeed, HiveMQ provides onboarding programs, certifications, and educational content to establish this common language.

Use Case

One specific use case we’d like to highlight is that at Lilly, a Pharmaceutical company:

Getting Started with HiveMQ & UNS

Dominik shared practical advice for companies just starting out:

* Begin with open-source HiveMQ Edge and Cloud—no license or sales team required.

* Start small—connect one PLC, stream one tag, and build from there.

* Demonstrate value quickly—show how a single insight (like predicting downtime from a temperature drift) can justify further investment.

* Then scale—build a sustainable, standards-based data architecture with the support of experienced partners.

Final Thoughts: A Fitting End to the Series

This episode was the perfect way to end our Industrial DataOps podcast series—a conversation that connected the dots between open standards, scalable data architecture, organizational design, and future-ready analytics (and don’t worry, we have lots of other podcast ideas for the months to come :)).

HiveMQ’s journey—from a small startup to powering the largest industrial IoT deployments in the world—is proof that open, scalable, and reliable infrastructure will be the foundation for the next generation of digital manufacturing.

If you want to learn more about MQTT, UNS, or HiveMQ Pulse, check out the excellent content at www.hivemq.com or their article on DataOps.

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 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.

Welcome to Episode 11!

As we get closer to Hannover Messe 2025, we’re also approaching the final episodes of this podcast series. Today we have two fantastic guests from AVEVA: Roberto Serrano Hernández, Technology Evangelist for the CONNECT industrial intelligence platform, and Clemens Schönlein, Technology Evangelist for AI and Analytics.

Together, they bring a unique mix of deep technical insight, real-world project experience, and a passion for making industrial data usable, actionable, and valuable.

We cover a lot in this episode: from the evolution of AVEVA's CONNECT industrial intelligence platform, to real-world use cases, data science best practices, and the cloud vs. on-prem debate. It’s a powerful conversation on how to build scalable, trusted, and operator-driven data solutions.

Thanks for reading The IT/OT Insider! Subscribe for free to receive new posts.

What is CONNECT?

Let’s start with the big picture. What is the CONNECT industrial intelligence platform? As Roberto explains:

"CONNECT is an open and neutral industrial data platform. It brings together all the data from AVEVA systems—and beyond—and helps companies unlock value from their operational footprint."

This isn’t just another historian or dashboard tool. CONNECT is a cloud-native platform that allows manufacturers to:

* Connect to on-prem systems.

* Store, contextualize, and analyze data.

* Visualize it with built-in tools or share it with AI platforms like Databricks.

* Enable both data scientists and domain experts to collaborate on decision-making.

It’s also built to make the transition to cloud as seamless as possible—while preserving compatibility with legacy systems.

"CONNECT is for customers who want to do more – close the loop, enable AI, and future-proof their data strategy"

Where CONNECT Fits in the Industrial Data Capability Map

Roberto breaks it down neatly:

* Data Acquisition – Strong roots in industrial protocols and legacy system integration.

* Data Storage and Delivery – The core strength of CONNECT: clean, contextualized, and trusted data in the cloud.

* Self-Service Analytics & Visualization – Tools for both data scientists and OT operators to work directly with data.

* Ecosystem Integration – CONNECT plays well with Databricks, Snowflake, and other analytics platforms.

But Clemens adds an important point:

"The point isn’t just analytics—it’s about getting insights back to the operator. You can’t stop at a dashboard. Real value comes when change happens on the shop floor."

Use Case Spotlight: Stopping Downtime with Data Science at Amcor

One of the best examples of CONNECT in action is the case of Amcor, a global packaging manufacturer producing the plastic film used in things like chip bags and blister packs.

The Problem:

* Machines were stopping unpredictably, causing expensive downtime.

* Traditional monitoring couldn’t explain why.

* Root causes were hidden upstream in the process.

The Solution:

* CONNECT was used to combine MES data and historian data in one view.

* Using built-in analytics tools, the team found that a minor drift in a temperature setpoint upstream was causing the plastic’s viscosity to change—leading to stoppages further down the line.

* They created a correlation model, mapped it to ideal process parameters, and fed the insight back to operators.

"The cool part was the speed," said Clemens. "What used to take months of Excel wrangling and back-and-forth can now be done in minutes."

The Human Side of Industrial Data: Start with the Operator

One of the most powerful themes in this episode is the importance of human-centric design in analytics.

Clemens shares from his own experience:

"I used to spend months building an advanced model—only to find out the data wasn't trusted or the operator didn’t care. Now I start by involving the operator from Day 1."

This isn’t just about better UX. It’s about:

* Getting faster buy-in.

* Shortening time-to-value.

* Ensuring that insights are actionable and respected.

Data Management and Scaling Excellence

We also touched on the age-old challenge of data management. AVEVA’s take? Don’t over-architect. Start delivering value.

"Standardization is important—but don’t wait five years to get it perfect. Show value early, and the standardization will follow."

And when it comes to building centers of excellence, Clemens offers a simple yet powerful principle:

"Talk to the people who press the button. If they don’t trust your model, they won’t use it."

Final Thoughts

As we edge closer to Hannover Messe, and to the close of this podcast series, this episode with Clemens and Roberto reminds us what Industrial DataOps is all about:

* Useful data

* Actionable insights

* Empowered people

* Scalable architecture

If you want to learn more about AVEVA's CONNECT industrial intelligence platform and their work in AI and ET/OT/IT convergence, visit: www.aveva.com

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 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.