The IT/OT Insider Podcast with David and Willem

Welcome to Episode 10 of the IT/OT Insider Podcast. Today, we're pleased to feature Anupam Gupta, Co-Founder & President North Americas at Celebal Technologies, to discuss how enterprise systems, AI, and modern data architectures are converging in manufacturing.

Celebal Technologies is a key partner of SAP, Microsoft, and Databricks, specializing in bridging traditional enterprise IT systems with modern cloud data and AI innovations. Unlike many of our past guests who come from a manufacturing-first perspective, Celebal Technologies approaches the challenge from the enterprise side—starting with ERP and extending into industrial data, AI, and automation.

Anupam's journey began as a developer at SAP, later moving into consulting and enterprise data solutions. Now, with Celebal Technologies, he is helping manufacturers combine ERP data, OT data, and AI-driven insights into scalable Lakehouse architectures that support automation, analytics, and business transformation.

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ERP as the Brain of the Enterprise

One of the most interesting points in our conversation was the role of ERP (Enterprise Resource Planning) systems in manufacturing.

"ERP is the brain of the enterprise. You can replace individual body parts, but you can't transplant the brain. The same applies to ERP—it integrates finance, logistics, inventory, HR, and supply chain into a single system of record."

While ERP is critical, it doesn't cover everything. The biggest gap? Manufacturing execution and OT data.

* ERP handles business transactions → orders, invoices, inventory, financials.

* MES and OT systems handle operations → machine status, process execution, real-time sensor data.

Traditionally, these two have been separated, but modern manufacturers need both worlds to work together. That's where integrated data platforms come in.

Bridging Enterprise IT and Manufacturing OT

Celebal Technologies specializes in merging enterprise and industrial data, bringing IT and OT together in a structured, scalable way.

Anupam explains: "When we talk about Celebal Tech, we say we sit at the right intersection of traditional enterprise IT and modern cloud innovation. We understand ERP, but we also know how to integrate it with IoT, AI, and automation."

Key focus areas include:

* Unifying ERP, MES, and OT data into a central Lakehouse architecture.

* Applying AI to optimize operations, logistics, and supply chain decisions.

* Enabling real-time data processing at the edge while leveraging cloud for scalability.

This requires a shift from traditional data warehouses to modern Lakehouse architectures—which brings us to the next big topic.

What is a Lakehouse and Why Does It Matter?

Most people are familiar with data lakes and data warehouses, but a Lakehouse combines the best of both.

Traditional Approaches:

* Data warehouses → Structured, governed, and optimized for business analytics, but not flexible for AI or IoT data.

* Data lakes → Can store raw data from many sources but often become data swamps—difficult to manage and analyze.

Lakehouse Benefits:

* Combines structured and unstructured data → Supports ERP transactions, sensor data, IoT streams, and documents in a single system.

* High performance analytics → Real-time queries, machine learning, and AI workloads.

* Governance and security → Ensures data quality, lineage, and access control.

"A Lakehouse lets you store IoT and ERP data in the same environment while enabling AI and automation on top of it. That's a game-changer for manufacturing."

Celebal Tech is a top partner for Databricks and Microsoft in this space, helping companies migrate from legacy ERP systems to modern AI-powered data platforms.

There's More to AI Than GenAI

With all the hype around Generative AI (GenAI), it's important to remember that AI in manufacturing goes far beyond chatbots and text generation.

"Many companies are getting caught up in the GenAI hype, but the real value in manufacturing AI comes from structured, industrial data models and automation."

Celebal Tech is seeing two major AI trends:

* AI for predictive maintenance and real-time analytics → Using sensor and operational data to predict failures, optimize production, and automate decisions.

* AI-driven automation with agent-based models → AI is moving from just providing recommendations to executing complex tasks in ERP and MES environments.

GenAI has a role to play, but:

* Many companies are converting structured data into unstructured text just to apply GenAI—which doesn't always make sense.

* Enterprises need explainability and trust before AI can take over critical operations.

"Think of AI in manufacturing like self-driving cars—we're not fully autonomous yet, but we're moving toward AI-assisted automation."

The key to success? Good data governance, well-structured industrial data, and AI models that operators can trust.

Final Thoughts: Scaling DataOps and AI in Manufacturing

For manufacturers looking to modernize their data strategy, Anupam offers three key takeaways:

* Unify ERP and OT data → AI and analytics only work when data is structured and connected across systems.

* Invest in a Lakehouse approach → It's the best way to combine structured business data with real-time industrial data.

* AI needs governance→ Without trust, transparency, and explainability, AI won't be adopted at scale.

"You don't have to replace your ERP or MES, but you do need a data strategy that enables AI, automation, and better decision-making."

If you want to learn more about Celebal Technologies and how they're bridging AI, ERP, and manufacturing data, visit www.celebaltech.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!

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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.

Welcome to Episode 9 in our Special DataOps series. We’re getting closer to Hannover Messe, and thus also the end of this series. We still have some great episodes ahead of us, with AVEVA, HiveMQ and Celebal Technologies joining us in the days to come (and don’t worry, this is not the end of our podcasts, many other great stories are already recorded and will be aired in April!)

In this episode, we’re joined by David Rogers, Senior Solutions Architect at Databricks, to explore how AI, data governance, and cloud-scale analytics are reshaping manufacturing.

David has spent years at the intersection of manufacturing, AI, and enterprise data strategy, working at companies like Boeing and SightMachine before joining Databricks. Now, he’s leading the charge in helping manufacturers unlock value from their data—not just by dumping it into the cloud, but by structuring, governing, and applying AI effectively.

Databricks is one of the biggest names in the data and AI space, known for lakehouse architecture, AI workloads, and large-scale data processing. But how does that apply to the shop floor, supply chain, and industrial operations?

That’s exactly what we’re unpacking today.

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What is Databricks and How Does It Fit into Manufacturing?

Databricks is a cloud-native data platform that runs on AWS, Azure, and Google Cloud, providing an integrated set of tools for ETL, AI, and analytics.

David breaks it down:

"We provide a platform for any data and AI workload—whether it’s real-time streaming, predictive maintenance, or large-scale AI models."

In the manufacturing context, this means:

* Bringing factory data into the cloud to enable AI-driven decision-making.

* Unifying different data types—SCADA, MES, ERP, and even video data—to create a complete operational view.

* Applying AI models to optimize production, reduce downtime, and improve quality.

"Manufacturers deal with physical assets, which means their data comes from machines, sensors, and real-world processes. The challenge is structuring and governing that data so it’s usable at scale."

Why Data Governance Matters More Than Ever

Governance is becoming a critical challenge in AI-driven manufacturing.

David explains why:

"AI is only as good as the data feeding it. If you don’t have structured, high-quality data, your AI models won’t deliver real value."

Some key challenges manufacturers face:

* Data silos → OT data (SCADA, historians) and IT data (ERP, MES) often remain disconnected.

* Lack of lineage → Companies struggle to track how data is transformed, making AI deployments unreliable.

* Access control issues → Manufacturers work with multiple vendors, suppliers, and partners, making data security and sharing complex.

Databricks addresses this through Unity Catalog, an open-source data governance framework that helps manufacturers:

* Control access → Manage who can see what data across the organization.

* Track data lineage → Ensure transparency in how data is processed and used.

* Enforce compliance → Automate data retention policies and regional data sovereignty rules.

"Data governance isn’t just about security—it’s about making sure the right people have access to the right data at the right time."

A Real-World Use Case: AI-Driven Quality Control in Automotive

One of the best examples of how Databricks is applied in manufacturing is in the automotive industry, where manufacturers are using AI and multimodal data to improve yield of battery packs for EV’s.

The Challenge:

* Traditional quality control relies heavily on human inspection, which is time-consuming and inconsistent.

* Sensor data alone isn’t enough—video, images, and even operator notes play a role in defect detection.

* AI models need massive, well-governed datasets to detect patterns and predict failures.

The Solution:

* The company ingested data from SCADA, MES, and video inspection cameras into Databricks.

* Using machine learning, they automatically detected defects in real time.

* AI models were trained on historical quality failures, allowing the system to predict when a defect might occur.

* All of this was done at cloud scale, using governed data pipelines to ensure traceability.

"Manufacturers need AI that works across multiple data types—time-series, video, sensor logs, and operator notes. That’s the future of AI in manufacturing."

Scaling AI in Manufacturing: What Works?

A big challenge for manufacturers is moving beyond proof-of-concepts and actually scaling AI deployments.

David highlights some key lessons from successful projects:

* Start with the right use case → AI should be solving a high-value problem, not just running as an experiment.

* Ensure data quality from the beginning → Poor data leads to poor AI models. Structure and govern your data first.

* Make AI models explainable → Black-box AI models won’t gain operator trust. Make sure users can understand how predictions are made.

* Balance cloud and edge → Some AI workloads belong in the cloud, while others need to run at the edge for real-time decision-making.

"It’s not about collecting ALL the data—it’s about collecting the RIGHT data and applying AI where it actually makes a difference."

Unified Namespace (UNS) and Industrial DataOps

David also touches on the role of Unified Namespace (UNS) in structuring manufacturing data.

"If you don’t have UNS, your data will be an unstructured mess. You need context around what product was running, on what line, in what factory."

In Databricks, governance and UNS go hand in hand:

* UNS provides real-time context at the factory level.

* Databricks ensures governance and scalability at the enterprise level.

"You can’t build scalable AI without structured, contextualized data. That’s why UNS and governance matter."

Final Thoughts: Where is Industrial AI Heading?

* More real-time AI at the edge → AI models will increasingly run on local devices, reducing cloud dependencies.

* Multimodal AI will become standard → Combining sensor data, images, and operator inputs will drive more accurate predictions.

* AI-powered data governance → Automating data lineage, compliance, and access control will be a major focus.

* AI copilots for manufacturing teams → Expect more AI-driven assistants that help operators troubleshoot issues in real time.

"AI isn’t just about automating decisions—it’s about giving human operators better insights and recommendations."

Final Thoughts

AI in manufacturing is moving beyond hype and into real-world deployments—but the key to success is structured data, proper governance, and scalable architectures.

Databricks is tackling these challenges by bringing AI and data governance together in a platform designed to handle industrial-scale workloads.

If you’re interested in learning more, check out www.databricks.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:

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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.

Welcome to Episode 8 of the IT/OT Insider Podcast. Today, we’re diving into real-time data, edge processing, and AI-driven analytics with Evan Kaplan, CEO of InfluxData.

InfluxDB is one of the most well-known time-series databases, used by developers, industrial companies, and cloud platforms to manage high-volume data streams. With 1.3 million open-source users and partners like Siemens, Bosch, and Honeywell, it’s a major player in the Industrial DataOps ecosystem.

Evan brings a unique perspective—coming from a background in networking, cybersecurity, and venture capital, he understands both the business and technical challenges of scaling industrial data infrastructure.

In this episode, we explore:

* How time-series data has become critical in manufacturing.

* The shift from on-prem to cloud-first architectures.

* The role of open-source in industrial data strategies.

* How AI and automation are reshaping data-driven decision-making.

Let’s dive in.

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From Networking to Time-Series Data

Evan’s journey into time-series databases started in venture capital, where he met Paul Dix, the founder of InfluxData.

"At the time, I wasn't a data expert, but I saw an opportunity—everything in the world runs on time-series data. Sensors, machines, networks—they all generate metrics that change over time."

At the time, InfluxDB was a small open-source project with about 3,000 users. Today, it’s grown to 1.3 million users, powering everything from IoT devices and industrial automation to financial services and network telemetry.

One of the biggest drivers of this growth? Industrial IoT.

"Over the last decade, we’ve seen a shift. IT teams originally used InfluxDB for monitoring servers and applications. But today, over 60% of our business comes from industrial IoT and sensor data analytics."

How InfluxDB Maps to the Industrial Data Platform Capability Model

We often refer to our Industrial Data Platform Capability Map to understand where different technologies fit into the IT/OT data landscape.

So where does InfluxDB fit?

* Connectivity & Ingest → One of InfluxDB’s biggest strengths. It can ingest massive amounts of data from sensors, PLCs, MQTT brokers, and industrial protocols using Telegraf, their open source agent.

* Edge & Cloud Processing → Data can be stored and analyzed locally at the edge, then replicated to the cloud for long-term storage.

* Time-Series Analytics → InfluxDB specializes in storing, querying, and analyzing time-series data, making it ideal for predictive maintenance, OEE tracking, and process optimization.

* Integration with Data Lakes & AI → Many manufacturers use InfluxDB as the first stage in their data pipeline before sending data to Snowflake, Databricks, or other lakehouse architectures.

"Our strength is in real-time streaming and short-term storage. Most customers eventually downsample and push long-term data into a data lake."

A Real-World Use Case: ju:niz Energy’s Smart Battery Systems

One of the most compelling use cases for InfluxDB comes from ju:niz Energy, a company specializing in off-grid energy storage.

The Challenge:

* ju:niz needed to monitor and optimize distributed battery systems used in renewable energy grids.

* Each battery had hundreds of sensors generating real-time data.

* Connectivity was unreliable, meaning data couldn’t always be sent to the cloud immediately.

The Solution:

* Each battery system was equipped with InfluxDB at the edge to store and process local data.

* Data was compressed and synchronized with the cloud whenever a connection was available.

* AI models used InfluxDB data to predict battery failures and optimize energy usage.

The Results:

* Improved energy efficiency—By analyzing real-time data, ju:niz optimized battery charging and discharging across their network.

* Reduced downtime—Predictive maintenance prevented unexpected failures.

* Scalability—The system could be expanded without requiring a centralized cloud-only approach.

"This hybrid edge-cloud model is becoming more common in industrial IoT. Not all data needs to live in the cloud—sometimes, local processing is faster, cheaper, and more reliable."

Cloud vs. On-Prem: The Future of Industrial Data Storage

A common debate in industrial digitalization is whether to store data on-premise or in the cloud.

Evan sees a hybrid approach as the future:

"Pushing all data to the cloud isn’t practical. Factories need real-time decision-making at the edge, but they also need centralized visibility across multiple sites."

A few key trends:

* Cloud adoption is growing, with 55-60% of InfluxDB deployments now cloud-based.

* Hybrid architectures are emerging, where real-time data stays at the edge while historical data moves to the cloud.

* Data replication is becoming the norm, ensuring that insights aren’t locked into one location.

"The most successful companies are balancing edge processing with cloud-scale analytics. It’s not either-or—it’s about using the right tool for the right job."

AI and the Next Evolution of Industrial Automation

AI has been a major topic in every recent IT/OT discussion, but how does it apply to manufacturing and time-series data?

Evan believes AI will redefine industrial operations—but only if companies structure their data properly.

"AI needs high-quality, well-governed data to work. If your data is a mess, your AI models will be a mess too."

Some key AI trends he sees:

* AI-assisted predictive maintenance → Combining sensor data, historical trends, and real-time analytics to predict failures before they happen.

* Real-time anomaly detection → AI models can identify subtle changes in machine behavior and flag potential issues.

* Autonomous process control → Over time, AI will move from making recommendations to fully automating factory adjustments.

"Right now, AI is mostly about decision support. But in the next five years, we’ll see fully autonomous manufacturing systems emerging."

Final Thoughts: How Should Manufacturers Approach Data Strategy?

For companies starting their Industrial DataOps journey, Evan has a few key recommendations:

* Start with a strong data model → Don’t just collect data—structure it properly from day one.

* Invest in developers → The best data strategies aren’t IT-led or OT-led—they’re developer-led.

* Think hybrid → Balance edge and cloud storage to get the best of both worlds.

* Prepare for AI → Even if AI isn’t a priority now, organizing your data properly will make AI adoption easier in the future.

"Industrial data is evolving fast, but the companies that structure and govern their data properly today will have a huge advantage tomorrow."

Next Steps & More Resources

Industrial DataOps is no longer just a concept—it’s becoming a business necessity. Companies that embrace scalable data management and AI-driven insights will outpace competitors in efficiency and innovation.

If you want to learn more about InfluxDB and time-series data strategies, visit www.influxdata.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:

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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.

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.”

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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.

Welcome to Episode 6 of our Industrial DataOps podcast series. Today, we’re diving into a conversation with Joel Jacob, Principal Product Manager at Splunk, about the company’s growing focus on OT, its approach to industrial data analytics, and how it fits into the broader ecosystem of industrial platforms.

Splunk is a name that’s well known in IT and cybersecurity circles, but its role in industrial environments is less understood. Now, as part of Cisco, Splunk is positioning itself at the intersection of IT observability, security, and industrial data analytics. This episode is all about understanding what that means in practice.

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From IT and Cybersecurity to Industrial Data

Joel’s journey into Splunk mirrors the company’s shift into OT. Coming from a background in robotics, automotive, and smart technology, he initially saw Splunk as a security and IT analytics company. But what he found was a growing demand from industrial customers who were already using Splunk for OT use cases.

"A lot of customers had already started using Splunk for OT, and the company realized it needed people with industrial experience to support that growing demand."

Splunk has built its reputation on handling log data, security monitoring, and IT observability. But as Joel explains, industrial data has its own challenges, and Splunk has had to adapt.

How Splunk Fits into the Industrial Data Platform Capability Map

To make sense of where Splunk fits, we look at our Industrial Data Platform Capability Map—a framework that defines the core building blocks of an industrial data strategy.

Splunk’s Strengths:

* Data Storage and Analytics: This is where Splunk is strongest. The platform can ingest, store, and analyze massive amounts of data, whether it’s sensor data, log files, or security events.

* Data Quality and Federation: Splunk allows companies to store raw data and extract value dynamically, rather than forcing them to clean and standardize everything upfront. Its federated search capabilities also mean that data doesn’t have to be centralized—a key advantage for IT/OT integration.

* Visualization and Dashboards: With Dashboard Studio, Splunk provides modern, customizable visualizations that stand out from traditional industrial software.

Where Splunk is Expanding:

* Connectivity and Edge Computing: Historically, getting industrial data into Splunk required external middleware. But in the last 18 months, the company has introduced an edge computing device with built-in AI capabilities, making it easier to ingest and process OT data directly.

* Edge Analytics and AI: The Splunk Edge Hub enables local AI inferencing and analytics on industrial equipment, addressing latency and connectivity challenges that arise when relying on cloud-based models.

Joel sees this as a natural evolution:

"We know that moving all industrial data to the cloud isn’t always practical. By adding edge computing capabilities, we make it easier for OT teams to process data where it’s generated."

A Real-World Use Case: Energy Optimization in Cement Manufacturing

One of Splunk’s key industrial customers, Cementos Argos, is a major cement producer facing a common challenge—high energy costs and carbon emissions.

The Problem:

* Cement manufacturing is one of the most energy-intensive industries in the world.

* The company needed a way to optimize kiln operations while ensuring consistent product quality.

* Traditional manual adjustments were slow and lacked real-time visibility.

The Solution:

* The company ingested data from OT systems into Splunk.

* Using the Machine Learning Toolkit, they built predictive models to optimize kiln temperature and pressure settings.

* These models were then pushed back to PLCs, allowing automated process adjustments.

The Results:

* $10 million in annual energy savings across multiple sites.

* The ability to push AI models to the edge reduced response times by 20%.

* Operators could now trust AI-generated recommendations, while still overriding changes if needed.

"The combination of machine learning and real-time process control created a true closed-loop optimization system."

Federated Search: A Different Approach to Industrial Data

One of Splunk’s unique contributions to industrial data management is federated search. Unlike traditional platforms that require all data to be centralized, Splunk allows companies to analyze data across multiple sources in real-time.

Joel explains the shift in thinking:

"Most industrial data strategies assume you need a single source of truth. But in reality, data lives in multiple places, and moving it all is expensive. With federated search, we can analyze data wherever it resides—whether it’s on-prem, in the cloud, or at the edge."

This is a major departure from the “data lake” approach that many industrial companies have pursued. Instead of trying to move and harmonize all data upfront, Splunk’s model is about leaving data where it makes the most sense and analyzing it dynamically.

How IT and OT Collaboration is Changing

Bridging the IT/OT divide has been a theme across this podcast series, and Splunk’s approach to security and data federation provides a unique perspective on this challenge.

Joel shares some key insights on what makes collaboration successful:

* Security is often the bridge. Since IT teams already use Splunk for security monitoring, they are more open to OT data integration when it’s part of a broader cybersecurity strategy.

* OT needs tools that don’t slow them down. Engineers don’t want to wait for IT approval to test new models. That’s why Splunk’s edge device was designed to be easily deployable by OT teams.

* The next generation of engineers is more IT-savvy. Younger engineers entering the workforce are more comfortable with IT tools and cloud environments, making collaboration easier.

One of the most interesting points was how Splunk leverages its Cisco partnership to expand into OT environments:

"Cisco has an enormous footprint in industrial networking. By running analytics on Cisco switches and edge devices, we can make OT data integration seamless."

The Role of AI in Industrial Data

Like many companies, Splunk is exploring the role of AI and generative AI in industrial environments. One of the most promising areas is automating data analysis and dashboard creation.

Joel shares how this is already happening:

* AI-generated dashboards: Engineers can simply describe what they want in natural language, and Splunk’s AI generates the necessary queries and visualizations.

* Low-code model deployment: Instead of manually writing Python scripts, users can export machine learning models with a single click.

* Multimodal AI: By combining sensor data, image recognition, and sound analysis, AI models can detect patterns that human operators might miss.

"In the next few years, AI will make it dramatically easier to analyze and visualize industrial data—without requiring deep programming expertise."

Final Thoughts

Splunk’s journey into OT is a great example of how traditional IT platforms are adapting to the realities of industrial environments. While the company’s core strength remains in data analytics and security, its expansion into edge computing and OT integration is opening up new possibilities for manufacturers.

If you want to learn more about how Splunk is evolving in the OT space, check out their website: www.splunk.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.

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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.

Welcome to another episode of the IT/OT Insider Podcast. In this special series on Industrial DataOps, we’re diving into the world of real-time industrial data, edge computing, and scaling digital transformation. Our guest today is John Younes, Co-founder and COO of Litmus, a company that has been at the forefront of industrial data platforms for the past 10 years.

Litmus is a name that keeps popping up when we talk about bridging OT and IT, democratizing industrial data, and making edge computing scalable. But what does that actually mean in practice? And how does Litmus help manufacturers standardize and scale their industrial data initiatives across multiple sites?

That’s exactly what we’re going to explore today.

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Litmus, you say?

John introduces Litmus as an Industrial DataOps platform, designed to be the industrial data foundation for manufacturers. The goal? To make industrial data usable, scalable, and accessible across the entire organization.

"We help manufacturers connect to any type of equipment, normalize and store data locally, process it at the edge, and then integrate it into enterprise systems—whether that’s cloud, AI platforms, or business applications."

At the core of Litmus’ offering is Litmus Edge, a factory-deployable edge data platform. It allows companies to:

* Connect to industrial equipment using built-in drivers.

* Normalize and store data locally, enabling real-time analytics and processing.

* Run AI models and analytics workflows at the edge for on-premise decision-making.

* Push data to cloud platforms like Snowflake, Databricks, AWS, and Azure.

For enterprises with multiple factories, Litmus Edge Manager provides a centralized way to manage and scale deployments, allowing companies to standardize use cases across multiple plants.

"We don’t just want to collect data. We want to help companies actually use it—to make better decisions and improve efficiency."

How Litmus Maps to the Industrial Data Platform Capability Model

We always refer to our Industrial Data Platform Capability Map to understand how different technologies fit into the broader IT/OT data landscape. So where does Litmus fit in?

* Connectivity → One of Litmus’ core strengths. Their platform connects to PLC, SCADA, MES, historians, and IoT sensors out-of-the-box.

* Edge Compute and Store → Litmus processes and optionally stores data locally before sending it to the cloud, reducing costs and improving real-time responsiveness.

* Data Normalization & Contextualization → The platform includes a data modeling layer, making sure data is structured and usable for enterprise applications.

* Analytics & AI → Companies can run KPIs like OEE, asset utilization, and energy consumption directly on the edge.

* Scalability & Management → With Litmus Edge Manager, enterprises can deploy and scale their data infrastructure across dozens of plants without having to rebuild everything from scratch.

John explains:

"The biggest challenge in industrial data isn’t just connecting things—it’s making that data usable at scale. That’s why we built Litmus Edge Manager to help companies replicate use cases across their entire footprint."

A Real-World Use Case: Standardizing OEE Across 35 Plants

One of the most compelling Litmus deployments comes from a large European food & beverage manufacturer with 50+ factories.

The Challenge:

* The company had grown through acquisitions, meaning each factory had different equipment, different systems, and different data formats.

* They wanted to standardize OEE (Overall Equipment Effectiveness) across all plants to benchmark performance and identify inefficiencies.

* They needed a way to deploy an Industrial DataOps solution at scale—without taking years to implement.

The Solution:

* The company deployed Litmus Edge in 35 factories within 12-18 months.

* They standardized KPIs like OEE across all plants, providing real-time insights into performance.

* By filtering and compressing data at the edge, they reduced cloud storage costs by 90%.

* They also introduced energy monitoring, identifying unused machines running during non-production hours, leading to 4% energy savings per plant.

The Impact:

* Faster deployment: The project was rolled out with just a small team, proving that scalability in industrial data is possible.

* Cost savings: Less unnecessary cloud storage and lower energy usage translated to significant financial gains.

* Enterprise-wide visibility: For the first time, they could compare OEE across all plants and identify best practices for process optimization.

"With Litmus, they didn’t just deploy a one-off use case. They built a scalable, repeatable data foundation that they can expand over time."

The Challenge of Scaling Industrial Data

One of the biggest barriers to industrial digitalization is scalability. IT systems are designed to scale effortlessly—but factory environments are different.

John explains:

"Even within the same factory, two production lines might be completely different. How do you deploy a use case that works across all sites without starting from scratch every time?"

His answer? A standardized but flexible approach.

* 80% of the deployment can be standardized.

* 20% requires last-mile configuration to account for machine variations.

* A central management platform ensures that scaling doesn’t require an army of engineers.

"The key is having a platform that adapts to different machines and processes—without forcing companies to custom-build everything for each site."

Data Management: The Next Big IT/OT Challenge

As industrial companies push for enterprise-wide data strategies, data management is becoming a bigger issue.

John shares his take:

"IT teams have been doing data management for years. But in OT, data governance is still a new concept."

Some of the biggest challenges he sees:

* Legacy data formats and siloed systems make data hard to standardize.

* Different plants use different naming conventions, making data aggregation difficult.

* Lack of clear ownership—Who is responsible for defining the data model? IT? OT? Corporate?

To address this, Litmus introduced a Unified Namespace (UNS) solution, allowing companies to enforce data models from enterprise level down to individual assets.

"We’re seeing more companies set up dedicated data teams—because without good data management, AI and analytics won’t work properly."

The Role of AI in Industrial Data

AI is the hottest topic in manufacturing right now, but how does it actually fit into industrial data workflows?

John sees two major trends:

* AI-powered analytics at the edge

* Instead of just sending raw data to the cloud, companies are running AI models directly on edge devices.

* Example: AI detecting machine anomalies and recommending preventative actions to operators before failures occur.

* AI-assisted deployment & automation

* Litmus is using AI to simplify Industrial DataOps—automating edge deployments across multiple sites.

* Example: Instead of manually configuring devices, users can type a command like “Deploy Litmus Edge to 30 plants with Siemens drivers”, and the system automates the entire process.

"AI won’t replace humans on the shop floor anytime soon. But it will make deploying, managing, and using industrial data significantly easier."

Final Thoughts

Industrial DataOps is no longer just a technical experiment—it’s becoming a business necessity. Companies that don’t embrace scalable data management and AI-driven insights risk falling behind their competitors.

Litmus is tackling the problem head-on by providing a standardized but flexible way to ingest, process, and scale industrial data.

If you want to learn more about Litmus and their approach to Industrial DataOps, check out their website: www.litmus.io.

Continue reading here:

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:

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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.

Welcome to Episode 4 of our special podcast series on Industrial DataOps. Today, we’re joined by Aron Semle, CTO at HighByte, to discuss how contextualized industrial data, Unified Namespace (UNS), and Edge AI are transforming IT/OT collaboration.

Aron has spent over 15 years working in industrial connectivity, starting his career at Kepware (later acquired by PTC) before joining HighByte in 2020. With a deep understanding of industrial data integration, he shares insights on why DataOps matters, what makes or breaks a data strategy, and how organizations can scale their industrial data initiatives.

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Who is HighByte?

HighByte is focused on Industrial DataOps—helping companies connect, contextualize, and share industrial data at scale. The platform bridges the gap between OT and IT, ensuring that manufacturing data is structured, clean, and ready for enterprise systems.

Aron sums it up perfectly:

"We solved connectivity years ago, but we never put context around data. Industrial DataOps is about fixing that—so IT teams actually understand the data coming from OT systems."

This contextualization challenge is at the heart of Industrial DataOps, and it’s why companies are moving beyond simple connectivity toward structured, enterprise-ready industrial data.

What is Industrial DataOps?

Many organizations struggle with fragmented, unstructured data in manufacturing. Aron defines Industrial DataOps as:

* An IT-driven discipline applied to OT

* The process of structuring, transforming, and sharing industrial data

* A bridge between factory systems and enterprise applications

Unlike traditional IT DataOps tools, Industrial DataOps must handle:

* Unstructured, time-series data from OT systems

* Multiple industrial protocols (OPC UA, MQTT, Modbus, etc.)

* On-prem, edge, and cloud data architectures

In short, Industrial DataOps is not just about moving data—it’s about making it usable.

Mapping HighByte to the Industrial Data Platform Capability Model

In our podcast series, we’ve introduced the Industrial Data Platform Capability Map—a framework that helps organizations understand the building blocks of industrial data platforms.

Where Does HighByte Fit?

* Connectivity → HighByte ingests data from PLC, SCADA, MES, historians, databases, and files.

* Contextualization → HighByte’s core strength. It structures data into reusable models before sending it to IT.

* Data Sharing → The platform delivers industrial data in IT-ready formats for BI tools, data lakes, and analytics platforms.

* Storage, Analytics & Visualization → HighByte does not store data or provide analytics. Instead, it feeds high-quality data to existing enterprise tools.

Aron explains the reasoning behind this approach:

"If we started adding storage and visualization, we’d just compete with existing factory systems. Instead, we make sure they work better."

A Real-World Use Case: Detecting Stuck AGVs in Warehouses

One of HighByte’s customers—a global manufacturer with hundreds of warehouses—used Industrial DataOps to optimize autonomous guided vehicles (AGVs).

The Challenge:

* The company used multiple AGV vendors, each with different protocols (Modbus, OPC UA, MQTT).

* Some AGVs would get stuck in corners, causing downtime and inefficiencies.

* Operators had no way to detect when an AGV was stuck across multiple sites.

The Solution:

* HighByte created a standardized data model for AGVs across all sites.

* The platform unified AGV data from different vendors and protocols.

* AWS Lambda functions processed AGV data in real-time to detect and alert operators.

The Results:

* Operators received real-time alerts when AGVs got stuck.

* Downtime was minimized, improving warehouse efficiency.

* The solution was scalable across all sites, reducing integration costs.

Below is another example of the power of Industrial DataOps, in this case at their customer Gousto:

Unified Namespace (UNS): Buzzword or Game-Changer?

The concept of Unified Namespace (UNS) has exploded in popularity, but what does it actually mean?

According to Aron:

"A lot of people think of UNS as just MQTT and a broker, but it’s more than that. It’s a logical way to structure and contextualize industrial data—making it accessible across IT and OT."

Aron warns against over-engineering UNS:

"If you spend six months defining the perfect UNS model, but no one uses it, what did you actually achieve?"

Instead, he recommends a use-case-driven approach, where UNS evolves organically as new applications require structured data.

Scaling DataOps: What Makes or Breaks a Data Strategy?

Aron has seen countless industrial data projects, and he knows what works—and what doesn’t.

Signs of a Failing Data Strategy:

🚩 IT wants to push all factory data to the cloud without defining use cases.🚩 OT ignores IT and builds custom, local integrations that don’t scale.🚩 No executive sponsorship to drive alignment across teams.

What Works?

✅ IT and OT collaboration—creating a DataOps team that manages data models and flows.✅ Use-case-driven approach—focusing on practical business outcomes rather than just moving data.✅ Scalable architecture—ensuring that data pipelines can expand over time without major rework.

Aron summarizes:

"If IT and OT aren’t working together, your data strategy is doomed. The best companies build cross-functional teams that manage data, not just technology."

Edge AI: The Next Big Thing?

While most AI in manufacturing has focused on cloud-based analytics, Aron believes Edge AI will change the game—especially for real-time operator assistance.

What is Edge AI?

* AI models run locally on edge devices, rather than in the cloud.

* Reduces latency, data transfer costs, and security risks.

* Ideal for operator support, real-time recommendations, and process optimization.

Early Use Cases:

* Operator guidance—Providing real-time suggestions to improve efficiency.

* Process optimization—AI-driven adjustments to production settings.

* Fault detection—Identifying anomalies at the edge before failures occur.

While AI isn’t ready for fully closed-loop automation yet, Aron sees huge potential for AI-driven insights to help human operators make better decisions.

Final Thoughts & What’s Next?

We had an amazing discussion with Aron Semle, who shared insights on Industrial DataOps, UNS, Edge AI, and scaling industrial data strategies.

If you’re interested in learning more about HighByte, check out their website: www.highbyte.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 Episode 3 of our special podcast series on Industrial DataOps. Today, we’re excited to sit down with Andrew Waycott, President and Co-founder of TwinThread, to explore how AI and Digital Twins can transform manufacturing operations.

Andrew has been working with industrial data for over 30 years, from building MES and historian solutions to developing real-time AI-driven optimization at TwinThread. In this episode, we discuss the state of industrial data, the role of AI, and why closed-loop automation is the future of AI in manufacturing.

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What is TwinThread?

TwinThread was founded with a simple but powerful mission: Make AI accessible to non-technical engineers in manufacturing.

As Andrew explains:

"Most engineers in manufacturing shouldn’t have to become data scientists to solve industrial problems. TwinThread is about giving them AI-powered tools they can actually use."

The platform covers data ingestion, contextualization, AI analytics, and closed-loop optimization, all while allowing manufacturers to start small, scale fast, and operationalize AI without massive IT overhead.

Mapping TwinThread to the Industrial Data Platform Capability Model

For those following our podcast series, you know we’ve been refining our Industrial Data Platform Capability Map—a framework to understand how different vendors fit into the industrial data ecosystem. Andrew breaks it down step by step:

* Connectivity: TwinThread ingests data from a wide range of industrial systems—Historians, OPC, MES, databases, IoT platforms, and MQTT.

* Digital Twin & Contextualization: The platform structures data into Digital Twins, modeling not just assets, but also maintenance, production, and process relationships.

* Data Cleaning & Quality: TwinThread automates the process of cleaning, organizing, and adding context to industrial data.

* Data Storage: While TwinThread functions as a cloud historian, it doesn’t require companies to replace existing on-prem historians.

* Analytics: The core strength of TwinThread is its ability to analyze and optimize processes using AI, applying predictive models to industrial operations.

* Data Sharing: The platform generates curated datasets—ready for BI tools like PowerBI, Snowflake, or Databricks—allowing manufacturers to turn raw data into actionable insights.

* Visualization & Dashboards: Unlike traditional generic dashboards, TwinThread provides visual tools optimized for operational decision-making.

As Andrew puts it:

"We don’t just show data. We help you solve problems—whether that’s quality optimization, energy efficiency, or predictive maintenance."

A Real-World Use Case: Quality Optimization at Hills Pet Food

One of TwinThread’s most successful deployments is with Hill’s Pet Food (a Colgate company), where they’ve transformed quality control across all global production lines.

The Challenge:

* Dog and cat food requires strict control of moisture, fat, and protein levels to ensure product consistency and compliance.

* Manual adjustments led to variability, waste, and inefficiencies.

* Traditional sampling-based quality control meant problems were discovered too late—after bad batches were already produced.

The Solution:

* TwinThread integrates with Hill’s existing infrastructure, pulling data from historians and process control systems.

* Their Perfect Quality AI Module predicts final product quality in real time—before production is complete.

* The system automatically optimizes setpoints at the beginning of the line, ensuring the process always stays within ideal quality parameters.

The Results:

* No more bad batches—quality issues are detected and corrected before they occur.

* Maximized yield & cost efficiency, as AI continuously fine-tunes production to hit quality targets at the lowest possible cost.

* Scalability—The system is now running on 18 production lines worldwide.

And perhaps most impressively:

"We implemented a fully closed-loop, AI-powered quality control system—probably the first of its kind in the food industry."

Closed-Loop AI: The Key to Scalable Industrial Automation

Many companies struggle to move beyond pilot projects because AI-driven insights still require manual intervention. TwinThread changes that with closed-loop AI.

Instead of just providing insights, the system automatically adjusts process parameters to maintain optimal performance.

Andrew explains:

"A lot of people think closed-loop automation means making adjustments every millisecond. But in reality, most industrial processes don’t need real-time micro-adjustments—what they need is the ability to make controlled, intelligent changes at regular intervals."

At Hills Pet Food, AI-generated adjustments are sent directly to the control system, where operators can:

* Manually review recommendations before applying them.

* Auto-accept adjustments within pre-set limits.

Why Closed-Loop AI Matters:

* Eliminates the risk of “shelfware”—AI models that aren’t actively used often get abandoned.

* Ensures long-term impact—AI insights become part of daily operations, not just a one-time report.

* Frees up operators—Instead of constantly tweaking processes, they focus on higher-value tasks.

The IT/OT Divide: What Makes AI Projects Succeed?

One of the biggest barriers to AI adoption in manufacturing is organizational silos between IT and OT.

Red flags in AI projects?

* No IT/OT collaboration—When IT and OT teams don’t align, AI solutions often fail to scale beyond pilots.

* No senior-level sponsorship—Without executive buy-in, projects get stuck in proof-of-concept mode.

* Lack of automation maturity—Companies still manually tracking process variables on paper aren’t ready for advanced AI-driven optimization.

Andrew sees a major shift happening:

"Nine years ago, getting buy-in for AI in manufacturing was nearly impossible. Today, leadership teams actively want AI solutions—but they need a clear roadmap to operationalize them."

Standardization: The Next Big Challenge for Industrial AI

Despite advances in AI and cloud data storage, the industrial world still lacks standardized ways to store and structure data.

Andrew warns:

"Every company is reinventing the wheel—creating their own custom data lakes with unique structures. That makes it nearly impossible to build scalable, interoperable AI solutions."

Andrew suggests the industry needs a standardized approach to cloud-based industrial data storage—similar to how Sparkplug B standardized MQTT architectures.

Final Thoughts

We had a fantastic conversation with Andrew Waycott, who shared insights on AI, Digital Twins, and scaling industrial automation.

If you’re interested in learning more about TwinThread, check out their website: www.twinthread.com.

Or visit them at the Hannover Messe at the AWS Booth, Hall 15, Stand D76. More information can be found on the HMI website.

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!

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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 2 of our special podcast series on Industrial Data. Today, we’re joined by Martin Thunman, CEO and co-founder of Crosser. Together with David and Willem, we dive deep into Industrial DataOps, IT/OT integration, and how real-time processing is shaping the future of manufacturing.

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What is Crosser?

Crosser is a next-generation integration platform built specifically for industrial environments. It acts as the intelligent layer between OT, IT, cloud, and SaaS applications. As Martin puts it:

"We see ourselves as a combination of Industrial DataOps, next-generation iPaaS, and a real-time stream and event processing platform—all in one."

For those unfamiliar with iPaaS (Integration Platform as a Service), Martin explains how traditional integration platforms started with enterprise service buses (ESB), then evolved into cloud-based solutions. Crosser takes this further by integrating both industrial and enterprise data in a way that not only moves data but also processes and transforms it in real time.

Mapping Crosser to the Industrial Data Platform Capability Model

The Industrial Data Platform Capability Map was created to help companies make sense of the complex ecosystem of industrial data platforms. When asked where Crosser fits in, Martin identified key areas where they outperform:

* Connectivity: Crosser enables companies to connect to over 800 different systems, from ERP and MES to QMS and supply chain applications. However, Martin emphasizes that connectivity alone is not enough.

* Data in Motion & Transformation: Crosser doesn’t store data; instead, it enables real-time analytics and transformation at the edge. Martin notes:"If you have a platform that connects data, why not take the opportunity to do something with it while moving it?"

* Analytics: Companies are increasingly running machine learning models at the edge for anomaly detection, predictive maintenance, and real-time decision-making. Crosser enables closed-loop automation, where anomalies can trigger automatic machine stoppages or dynamic work order creation.

One area where Crosser can also help is in the "supporting capabilities", such as deployment, monitoring, and user management. Or in Martin’s words:

"Boring enterprise features like deployment and monitoring are actually critical when rolling out solutions across multiple sites."

A Real-World Use Case: Real-Time Anomaly Detection & Automated Work Orders

One concrete example of Crosser in action involves real-time anomaly detection in an industrial setting. Here’s how it works:

* Step 1: Data is collected in real-time from a plant historian with thousands of data tags.

* Step 2: Anomalies are detected using fixed rules or machine learning models at the edge.

* Step 3: If an issue is found, an automated work order is sent to SAP, triggering maintenance actions without human intervention.

This closed-loop automation prevents failures before they happen and reduces downtime.

Breaking Down IT and OT Silos

One of the biggest challenges in industrial digitalization is the disconnect between IT and OT teams. Martin highlights how modern industrial environments require collaboration between multiple skill sets:

* OT Teams → Understand machine data, sensors, and processes.

* Data Science Teams → Develop machine learning models.

* IT Teams → Manage cloud, enterprise systems, and security.

Traditionally, these groups have worked in silos, making IT/OT convergence difficult. Crosser’s low-code approach aims to bridge the gap, allowing different teams to collaborate on the same workflows.

"OT knows their machines, IT knows their systems, and data scientists know their models. The challenge is getting them to work together."

Final Thoughts & What’s Next?

We had a fantastic discussion with Martin Thunman, who shared valuable insights into the future of industrial data processing.

If you’re interested in learning more about Crosser, check out their website: www.crosser.io.

Stay Tuned for More!

Subscribe to our podcast and blog to stay up-to-date on the latest trends in Industrial Data, AI, and IT/OT convergence.

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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.

In the first episode (see video above), David and Willem take you behind the scenes of their Industrial Data Platform Capability Map—a structured way to understand how organizations can truly leverage their industrial data. David talks about the role of a platform and which capabilities are needed to build it. He also focuses on the role of Data Management and how that is linked to building a Unified Namespace.

Explore all 12 episodes here, on YouTube or on Spotify!

NEW! We just launched our ITOT.Academy. Learn the language and architecture of IT and OT to push past “just a POC” in our live online academy.

NEW! We just launched our ITOT.Academy. Learn the language and architecture of IT and OT to push past “just a POC” in our live online academy.

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If you are interested in Industrial Data, you should definitely review these earlier articles: Part 1 (The IT and OT view on Data), Part 2 (Introducing the Operational Data Platform), Part 3 (The need for Better Data), Part 4 (Breaking the OT Data Barrier: It's the Platform), Part 5 (The Unified Namespace) and Part 6 (The Industrial Data Platform Capability Map)