Industrial DataOps #7 with Rhize - Geoff Nunan on Why Data Modeling in Manufacturing isn’t optional

Welcome back to the IT/OT Insider Podcast. In this episode, we dive deep into industrial data modeling, manufacturing execution systems (MES), and the rise of headless data platforms with Geoff Nunan, CTO and co-founder of Rhize.

Geoff has been working in industrial automation and manufacturing information systems for over 30 years. His experience spans multiple industries, from mining and pharmaceuticals to food & beverage. But what really drove him to start Rhize was a frustration many in the industry will recognize:

"MES solutions are either too rigid or too custom-built. We needed a third option—something flexible but structured, something that could scale without requiring endless software development."

Rhize is built around that idea. It’s a headless manufacturing data platform that allows companies to build custom applications on top of a standardized data backbone.

In today’s discussion, we explore why MES implementations often struggle, why data modeling is key to digital transformation, and how companies can avoid repeating the same mistakes when scaling industrial data solutions. Or in the words of Geoff:

“Data Modeling in manufacturing isn't optional. You're either going to end up with the model that you planned for or the one that you didn’t.”

Thanks for reading The IT/OT Insider! Subscribe for free to support our work:

Why Geoff co-founded Rhize: The MES Dilemma

Geoff’s journey to starting Rhize began with a frustrating experience at a wine bottling plant in Australia.

The company was implementing an MES solution to track downtime, manage inventory, and integrate with ERP. Sounds simple, right? But the project quickly became complex and expensive—and despite being an off-the-shelf solution, it required a lot of custom development.

"It was a simple MES use case, yet we spent 80% of our time on the 20% of requirements that didn’t fit the system. That’s the reality of most MES projects."

After seeing this pattern repeat across multiple industries, Geoff realized the problem wasn’t just the software—it was the entire approach.

* Off-the-shelf MES systems are often too rigid → They don’t adapt well to company-specific workflows.

* Custom-built solutions are too complex → They require too much development and long-term maintenance, especially in larger corporations.

* Manufacturing data needs structure, but also flexibility → There wasn’t a “headless” option that let companies build custom applications on a standardized data backbone.

So, seven years ago, Geoff and his team started Rhize, focusing on providing a flexible, open manufacturing data platform that supports modern low-code front-end applications.

"We don’t provide an MES. We provide the data foundation that lets you build MES-like applications the way you need them."

How Rhize Maps to the Industrial Data Platform Capability Model

One of the key themes of our podcast series is understanding where different solutions fit into the broader industrial data ecosystem.

So, how does Rhize align with our Industrial Data Platform Capability Map?

* Data Modeling → The core of Rhize. It provides a structured, standardized manufacturing data model based on ISA-95.

* Connectivity → Connection via open API’s and the most important industrial protocols.

* Workflow & Event Processing → Supports rules-based automation and event-driven manufacturing processes.

* Scalability → Built to support multi-site deployments with a common, reusable data architecture.

"Traditional MES forces you into a rigid workflow. With Rhize, you get the structure of MES but the flexibility to adapt it to your needs."

The Importance of Data Modeling in Manufacturing

A recurring theme in our conversation is data modeling—a topic that IT teams understand well, but OT teams often overlook.

Geoff explains why a strong data model is critical for industrial data success:

"Any IT system lives or dies by how well its data is structured. Yet in manufacturing, we often take a 'just send the data somewhere' approach without thinking about how to organize it for long-term use."

The problem? Without a structured approach:

* Data becomes siloed → Every plant has a different data format and naming convention.

* Scaling becomes impossible → A solution that works in one factory won’t work in another without extensive rework.

* AI and analytics won’t deliver value → Without consistent, contextualized data, AI models struggle to provide reliable insights.

Geoff believes companies need to adopt structured industrial data models—and the best foundation for that is ISA-95.

"ISA-95 gives us a common language to describe manufacturing. If companies start with this as their foundation, they avoid years of painful restructuring later."

A Real-World Use Case: Gold Traceability in Luxury Watchmaking

One of Rhize’s projects involved a luxury Swiss watchmaker trying to solve a complex traceability problem.

The Challenge:

* The company uses different grades of gold in its watches.

* Due to fluctuating gold prices, tracking material usage accurately was critical.

* The company needed mass balance tracking across all factories, but each plant had different processes and equipment.

The Solution:

* They implemented Rhize as a standardized data platform across all factories.

* They modeled gold usage at a granular level, ensuring every gram was accounted for.

* By unifying data across sites, they could benchmark efficiency and reduce material waste.

The Result:

* Improved material traceability, reducing financial loss from inaccurate tracking.

* More efficient use of gold, leading to millions in savings per year.

* A scalable system, enabling future expansion to other materials and components.

"They didn’t just solve a traceability problem. They built a data foundation that can now be extended to other manufacturing processes."

Why MES Projects Fail—and How to Avoid It

One of the biggest takeaways from our conversation is why MES implementations struggle.

Geoff has seen companies fail multiple times before getting it right, often repeating the same mistakes:

* Overcomplicating the data model → Trying to design for every possible scenario upfront.

* Lack of standardization → Each site implements MES differently, making it impossible to scale.

* Not considering long-term flexibility → A system that works now may not work five years from now.

His advice?

"Companies need to move away from 'big bang' MES rollouts. Start with a strong data model, implement a scalable data platform, and build applications on top of that."

The Role of UNS in Data Governance

Unified Namespace (UNS) has been a hot topic in recent years, but how does it fit into manufacturing data management?

Geoff sees UNS as a useful tool, but not a silver bullet:

* It helps with real-time data sharing, but without a structured data model, it can quickly become a mess.

* Companies should see UNS as part of their data strategy, not the entire strategy.

"If you don’t start with a structured data model, UNS can become an uncontrolled stream of unstructured data. Governance is key."

Final Thoughts

Industrial data is evolving fast, but companies that don’t invest in proper data modeling will struggle to scale.

Rhize is tackling this problem by providing a structured but flexible data platform, allowing manufacturers to build applications the way they need—without the limitations of traditional MES.

If you want to learn more about Rhize and their approach to industrial data, visit www.rhize.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.

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