Industrial Data Analytics Services

What connects Industrial IoT, Application and Data Integration, and Process Intelligence? During my time at Software AG, my attention has shifted in line with the company's strategic priorities and the changing needs of the market. My focus on Industrial IoT, moved into Application and Data Integration, and now I specialise on Business Process Management and Process Intelligence through ARIS. While these areas may appear to address different challenges, a common thread runs through them. Take a typical production process as an example. From raw material intake to finished goods delivery, there are countless interdependencies, processes and workflows, and just as many data sources. Industrial IoT plays a key role by capturing real-time data from machines and sensors on the shop floor. This data provides visibility into equipment performance, production rates, energy usage, and more. It enables predictive maintenance, reduces downtime, and supports continuous improvement through real-time monitoring and analytics. Application and Data Integration brings together data from across the value chain, including sensor data, manufacturing execution systems, ERP platforms, quality management systems, logistics, and supply chain management. Synchronising these systems with integration creates a unified, reliable view of production operations. This cohesion is essential for automation, traceability, quality management and responsive decision-making across departments and geographies. Process Management, including modelling, and governance, risk, and controls, takes a different yet equally critical perspective. Modelling helps design optimal process flows, while governance frameworks ensure controls are in place to manage quality, risk, and enforce conformance for standardisation. Process mining uncovers bottlenecks, rework loops, and compliance deviations. It focuses on how the production process actually runs, rather than how it was designed to operate. Despite their different vantage points, each of these domains works toward the same goal: aggregating, normalising, and structuring data to transform it into information that can be easily consumed to create meaningful, actionable insights. If your organisation is capturing process-related data through isolated tools, such as diagramming or collaboration platforms, quality management systems, risk registers, or role-based work instructions, it is likely you are only seeing part of the picture. Without a unified approach to integrating and analysing this data, the deeper insights remain fragmented or out of reach. By aligning physical operations, applications & systems, and business processes, organisations can move beyond surface-level visibility to uncover the root causes of inefficiency, unlock hidden potential, and govern change with clarity and confidence. #Process #Intelligence #OperationalExcellence #QualityManagement #Risk #Compliance

Enabling Industrial Dataspaces on Azure Those of you who following me know that I’ve been leading a v-team of manufacturing experts at Microsoft for many years and that the dividends of our work have resulted in a tutorial on implementing an Industrial IoT reference solution architecture on Azure, based on industrial standards throughout. This has grown and grown into an end-to-end solution architecture and now includes more than 10 Azure services and many open-source reference apps on top, implementing use cases like automatic asset onboarding, asset monitoring, production line simulation, OEE calculation, Unified Name Space (UNS), yield predictions, anomaly detection, command & control from the cloud, predictive maintenance, 1-click automated deployments, enterprise resource planning via SAP, etc., etc., etc.   Therefore, it fills me with great joy to tell you that we have expanded the reference solution once again with the use cases of standardized, digital, and trusted data exchange for the manufacturing supply chain as well as “scope 2” Product Carbon Footprint (PCF) calculation. The format is once again a step-by-step tutorial describing how to enable industrial dataspaces on Azure. This allows data sharing across organizations as well as multiple different cloud infrastructures. Check it out: https://aka.ms/EDCAAS   I also want to specially callout the great folks at Fraunhofer IOSB who have contributed an important open-source reference implementation to the solution, the EDC-extension for the Asset Admin Shell: https://lnkd.in/e8inWKy5. Of course, the open-source components once again are built on international standards and run in Docker containers to enable interoperability and technology neutrality. Stay tuned for an extended version at Hannover Messe, integrating additional Azure services and covering scenarios like the upcoming EU Digital Product Passport (https://lnkd.in/eExVGHUs). #opcfoundation, #digitaltwinconsortium, #IDTA, #IDSA, #eclipsedataspacecomponents, #DataSpaces

The data sitting in your plant is both your biggest opportunity and your biggest challenge. For years, manufacturers have struggled with data silos—OT data (red) locked away in PLCs and SCADA systems, IT data (blue) living in ERPs and business systems. Two worlds that rarely speak to each other. The paradigm is shifting. Industrial Intelligence platforms are rewriting the rules - by achieving something previously thought impossible: maintaining the security and integrity of red and blue data separation while simultaneously unifying them for actionable insights. This isn't just theoretical. Manufacturers adopting this approach are seeing: → Real-time visibility across the entire operation—from shop floor to top floor → Predictive insights that prevent downtime before it happens → Data-driven decisions that move from reactive to proactive → Measurable ROI through reduced waste, improved OEE, and optimized production The key differentiator? Level 3 extensibility. By operating at the MES/MOM layer, these platforms bridge the gap between control systems and business systems, creating a unified intelligence layer that respects the boundaries of each domain while extracting maximum value from both. Digital transformation in manufacturing isn't about implementing more software. It's about creating an Industrial Intelligence foundation that turns disparate data streams into a single source of truth—one that drives continuous improvement and competitive advantage. The manufacturers winning today aren't choosing between OT and IT. They're unifying both.

🚨 Why Are Manufacturers Struggling to Adopt AI? 🚨 From streamlining operations and predicting maintenance to improving quality, AI has massive potential in manufacturing. But many manufacturers are hitting roadblocks. Why? It all comes down to data. 💡 AI is only as good as the data it’s built on—and that’s the challenge. For AI to work effectively, it needs reliable, contextual data from systems like ERP, MES, MOM, CMMS and SCM. But too often, data is siloed, inconsistent, or incomplete, limiting AI’s ability to deliver real insights (or value). 🔑 The Solution: Industrial Data Operations Industrial Data Ops allows manufacturers to aggregate, standardize, and contextualize data across all systems, ensuring AI has the right foundation to deliver actionable insights. Here’s how you can lay the groundwork: 📊 Consolidate data from ERP, MES, SCM, and more into one unified pipeline. 🔄 Ensure real-time data accuracy for AI models to work with. 🚀 Focus on Data Orchestration: AI won’t succeed without properly orchestrated data. Manufacturers need to make sure AI is working with the right information at the right time. ⚙️ The Bottom Line: AI programs will fail if they don't have reliable, contextual data. For manufacturers eager to adopt AI, it’s crucial to first build a strong data foundation through Industrial Data Ops and data orchestration. 💬 How are you preparing for AI in your manufacturing processes? Let’s discuss!👇 #AI #DataOps #Industry40 #SmartManufacturing #DigitalTransformation #DataDrivenManufacturing #MES #ERP #SCM

𝗧𝗵𝗲 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗗𝗮𝘁𝗮 𝗜𝗺𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲 -- 𝗜𝗧/𝗢𝗧 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗨𝗡𝗦 Industrial enterprises are facing the "data paradox", generating petabytes of operational data yet struggling to get real-time, contextualized insights. 𝗧𝗵𝗲 𝗜𝗧/𝗢𝗧 𝗗𝗶𝘃𝗶𝗱𝗲 For decades, #OT and #IT have been working separately due to priority differences: 🔸 𝗢𝗧 - Deterministic control, availability, and uptime. 🔸 𝗜𝗧 - Data storage, security, and scalability. This division led to "spaghetti architectures" following a hierarchical (PLC → SCADA/DCS → Historian → MES → ERP → Cloud → BI) and request-response model relying on hardcoded point-to-point integrations, with rigid and maintenance-heavy infrastructures creating single points of failure and several challenges: 🔸𝗣𝗼𝗹𝗹𝗶𝗻𝗴 𝗜𝗻𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝗶𝗲𝘀 – Cyclical polling (e.g., #OPC DA, #Modbus) introduces latency and creates unnecessary network load. 🔸𝗛𝗶𝗴𝗵 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘀𝘁𝘀 – Middleware solutions (e.g., #ETL pipelines, #APIs) require custom coding and maintenance. 🔸𝗗𝗮𝘁𝗮 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 & 𝗜𝗻𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 – Multiple, conflicting data versions emerge across IT and OT. 🔸𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗟𝗶𝗺𝗶𝘁𝗮𝘁𝗶𝗼𝗻𝘀 – Cloud #DataLakes and #historians struggle to synchronize with real-time, #edge-driven systems. 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗡𝗮𝗺𝗲𝘀𝗽𝗮𝗰𝗲 #UNS is a real-time, event-driven data architecture that centralizes all industrial data into a single, logical namespace, acting as a fully structured, hierarchical data model and a single source of truth that integrates IT, OT, edge, and #cloud ecosystems. Instead of having data residing in application-specific silos, UNS introduces a model that decouples producers and consumers, allowing systems to publish and subscribe to relevant data: 🔸 𝗘𝗱𝗴𝗲-𝗱𝗿𝗶𝘃𝗲𝗻 – All data sources publish updates as they occur. 🔸 𝗘𝘃𝗲𝗻𝘁-𝗯𝗮𝘀𝗲𝗱 – Enables push-based streaming. 🔸 𝗗𝗲𝗰𝗼𝘂𝗽𝗹𝗲𝗱 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 – Systems subscribe to relevant data without direct dependencies on other systems. 𝗞𝗲𝘆 𝗨𝗡𝗦 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 🔸 𝗠𝗤𝗧𝗧 - The de facto transport layer for UNS, enabling asynchronous, distributed, and scalable communications. The #SparkplugB extension tracks device online/offline states, normalizes data across heterogeneous device fleets, and notifies clients when devices go offline. 🔸 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝘆 – Structured, context-rich data organization (e.g., ISA-95 model: Enterprise → Site → Area → Line → Machine → Sensor). 🔸 𝗗𝗲𝗰𝗼𝘂𝗽𝗹𝗲𝗱 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝗲𝗮𝗺𝘀 – No hardcoded connections between systems; they interact dynamically as needed. UNS supports hybrid IT/OT deployments: 🔸𝗘𝗱𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 – Pre-processes high-frequency OT data before publishing it to UNS. 🔸𝗖𝗹𝗼𝘂𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 – #AI / #ML models can subscribe to edge-generated insights. ***** ▪ Follow me and ring the 🔔 to stay current on #Industry40 Insights!