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.
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.
If you’re visiting Hannover Messe, find them in Hall 16 Booth B06. More information here: https://litmus.io/hannover-messe
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.
Share this post