The IT/OT Insider Podcast - Pioneers & Pathfinders

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. Thanks for reading The IT/OT Insider! Subscribe for free to receive our weekly insights. 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 [https://itotinsider.substack.com/p/industrial-data-platform-capability]—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: https://www.highbyte.com/www.highbyte.com [https://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 [https://www.youtube.com/@TheITOTInsider] Apple Podcasts: Spotify Podcasts: Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Welcome to Episode 3 of our special podcast series on Industrial DataOps. Today, we’re excited to sit down with Andrew Waycott [https://www.linkedin.com/in/andrewwaycott/], President and Co-founder of TwinThread [https://www.twinthread.com/], 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. Subscribe to support our work and receive the other episodes directly in your mailbox :) 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 [https://itotinsider.substack.com/p/industrial-data-platform-capability]—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: https://www.twinthread.com/www.twinthread.com [http://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 [https://www.hannovermesse.de/exhibitor/twinthread/N1575971]. Stay Tuned for More! Subscribe to our podcast and blog to stay updated on the latest trends in Industrial Data, AI, and IT/OT convergence. 🚀 See you in the next episode! Youtube: https://www.youtube.com/@TheITOTInsider [https://www.youtube.com/@TheITOTInsider] Apple Podcasts: Spotify Podcasts: Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

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 [https://www.crosser.io/]. 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. Subscribe today to support our work and receive the next episodes as well! 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 [https://itotinsider.substack.com/p/industrial-data-platform-capability] 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: https://www.crosser.io/www.crosser.io [https://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. See you in the next episode! Youtube: https://www.youtube.com/@TheITOTInsider [https://www.youtube.com/@TheITOTInsider] Apple Podcasts: Spotify Podcasts: But also here on Substack: Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Welcome to Episode 1 of our special podcast series on Industrial Data. In this episode, David and Willem take you behind the scenes of their Industrial Data Platform Capability Map [https://itotinsider.substack.com/p/industrial-data-platform-capability]—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. But that's just the beginning! We’ve lined up a series of exciting conversations with industry tech leaders, showcasing their solutions and cutting-edge innovations in industrial data platforms. And the timing couldn’t be better: with Hannover Messe just around the corner, data will undoubtedly be one of the hottest topics on everyone’s mind. Subscribe now to receive every episode in this series on Industrial Data! If you are interested in Industrial Data, you should definitely review these earlier articles: Part [https://itotinsider.substack.com/p/the-it-and-ot-view-on-data-part-1]1 [https://itotinsider.substack.com/p/the-it-and-ot-view-on-data-part-1] (The IT and OT view on Data) [https://itotinsider.substack.com/p/the-it-and-ot-view-on-data-part-1], Part [https://itotinsider.substack.com/p/the-future-operational-data-platform-part-2]2 [https://itotinsider.substack.com/p/the-future-operational-data-platform-part-2] (Introducing the Operational Data Platform) [https://itotinsider.substack.com/p/the-future-operational-data-platform-part-2], Part [https://itotinsider.substack.com/p/the-need-for-better-data-why-data]3 [https://itotinsider.substack.com/p/the-need-for-better-data-why-data] (The need for Better Data) [https://itotinsider.substack.com/p/the-need-for-better-data-why-data], Part [https://itotinsider.substack.com/p/the-data-barrier-industrial-data-platform-p4]4 [https://itotinsider.substack.com/p/the-data-barrier-industrial-data-platform-p4] (Breaking the OT Data Barrier: It's the Platform) [https://itotinsider.substack.com/p/the-data-barrier-industrial-data-platform-p4], Part [https://itotinsider.substack.com/p/the-unified-namespace-uns-demystified]5 [https://itotinsider.substack.com/p/the-unified-namespace-uns-demystified] (The Unified Namespace) [https://itotinsider.substack.com/p/the-unified-namespace-uns-demystified] and Part [https://itotinsider.substack.com/p/industrial-data-platform-capability]6 [https://itotinsider.substack.com/p/industrial-data-platform-capability] (The Industrial Data Platform Capability Map [https://itotinsider.substack.com/p/industrial-data-platform-capability]) 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. See you in the next episode! Youtube: https://www.youtube.com/@TheITOTInsider [https://www.youtube.com/@TheITOTInsider] Apple Podcasts: Spotify Podcasts: But also here on Substack: This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Manufacturing has long been the backbone of global economies, yet the industry often remains hidden in plain sight, tucked away in industrial parks and misunderstood by the public. In this episode David was joined by Mike Nager. Mike Nager is a passionate advocate for Smart Manufacturing, with a career that began in electrical engineering, where he worked closely with manufacturers to automate and optimize their production processes. Over time, he visited hundreds of plants—ranging from automotive and pharmaceuticals to paper mills and tire factories—each with its own unique challenges and stories. From the carbon-black-coated environments of tire production to the ultra-clean rooms of semiconductor manufacturing, Mike witnessed firsthand the diversity of manufacturing and the dedication of the people behind the scenes. “It’s a world most people never get to see and part of my mission is to provide a window into that world.” He currently serves as a Business Development Executive at Festo Didactic, the technical education arm of the Festo Group, which provides equipment and solutions to prepare the workforce of tomorrow—a mission that’s more important now than ever. As if that weren’t enough, Mike is also an author, having published several engaging books, including All About Smart Manufacturing, a children’s book with delightful illustrations, and his Smart Student's Guide, aimed at helping students navigate the path to manufacturing careers. Thanks for reading The IT/OT Insider! Subscribe for free to receive new posts and episodes. Addressing the Awareness Gap One of Mike’s key messages is the need to bridge the “awareness gap” in manufacturing. For years, the perception of manufacturing as dirty, dangerous, and undesirable work has discouraged young people from pursuing these careers. However, as Mike explained, the tide is turning. Modern manufacturing offers high-paying, stable careers in fields like robotics, automation, and data analysis. We talked about how technical education can be a pathway to well-paying jobs, even for those without four-year degrees. “In some regions, students who complete just a year or two of technical training can go from earning minimum wage to $40 or $50 an hour with overtime,” he said. “It’s a massive opportunity for those who are willing to learn.” The Role of Education in Revitalizing Manufacturing As part of his work, Mike collaborates with educators to create hands-on training programs that prepare students for real-world manufacturing environments. Inspired by the German apprenticeship model, these programs emphasize learning by doing, providing students with the skills they need to succeed on the factory floor. Yet, as Mike pointed out, the U.S. education system faces unique challenges. Unlike Germany, where apprenticeships are embedded in the culture, the U.S. relies heavily on public education to develop technical skills. This gap in structured training has made it even more critical to create accessible and engaging educational resources. A Mission to Inspire—From High School to Children’s Books Mike has taken a creative approach to inspiring interest in manufacturing. In addition to his professional work, he’s authored a children’s book, All About Smart Manufacturing, and a high school-focused Smart Students Guide. These books introduce young readers to the possibilities of manufacturing careers, using relatable language and illustrations to make the subject approachable. “The first book was aimed at high school students, but I realized they’d already chosen their paths,” Mike explained. “That’s when I decided to write for younger kids, to plant the seed of curiosity early on.” The Future of Manufacturing Careers We also touched on broader trends shaping the industry, such as the push for local manufacturing due to national security concerns and the growing need for technical talent in an increasingly automated world. Mike emphasized that while automation is transforming processes, people remain at the heart of manufacturing. “The idea of a ‘lights-out factory’—completely automated with no people—has been talked about for decades. But in reality, people are still essential, and their roles are evolving to require more technical and analytical skills.” Closing Thoughts Mike’s passion for manufacturing and education is clear: from his hands-on work with educators to his mission of raising awareness through books and outreach. His vision for the future of manufacturing is one where education, automation, and human creativity come together to revitalize the industry. Or as Mike put it: “Manufacturing is one of the few industries that truly creates wealth. It’s not just about making things—it’s about building communities and creating opportunities.” Whether you’re an educator, a parent, or simply curious about the future of manufacturing, Mike’s insights are a valuable reminder of the importance of inspiring the next generation. As the industry evolves, it’s clear that the need for skilled, passionate people will only grow. Find Mike on LinkedIn: https://www.linkedin.com/in/mikenager/ [https://www.linkedin.com/in/mikenager/] Interested in one of his books? Printed and e-book versions available here: https://www.industrialinsightsllc.com/#books [https://www.industrialinsightsllc.com/#books] This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]