Importance of Industrial DataOps for Manufacturers

Here are two truths and a prediction for Industrial AI in 2025. 𝗧𝗿𝘂𝘁𝗵 #𝟭: 𝗔𝗜-𝗗𝗿𝗶𝘃𝗲𝗻 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗪𝗶𝗹𝗹 𝗗𝗿𝗶𝘃𝗲 𝗥𝗲𝗮𝗹 𝗩𝗮𝗹𝘂𝗲 The last two years have seen industrial AI mature significantly, transitioning from the hype to practical applications of LLMs and Agentic AI. While truly autonomous AI agents with advanced logic and reasoning may still be a distant goal for most, AI-driven process control is already creating measurable value. AI-driven process control’s momentum from just early adopters to mainstream adoption is a clear indicator of its growing role in industrial process optimization. 𝗧𝗿𝘂𝘁𝗵 #𝟮: 𝗗𝗮𝘁𝗮𝗢𝗽𝘀 𝗪𝗶𝗹𝗹 𝗕𝗲𝗰𝗼𝗺𝗲 𝗡𝗼𝗻-𝗡𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲 2024 was a really good year for Industrial DataOps, but 2025 will be great! Companies increasingly recognize that poor data quality means poor insights, no matter the sophistication of AI models. With data storage becoming increasingly commoditized, the emphasis is shifting toward organizing, contextualizing, and ensuring the quality of both real-time data streams and historical data. DataOps capabilities like unified namespaces and data governance are becoming foundational to manufacturing operations, driving better insights and more consistent decision-making. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻: 𝗗𝗲𝗮𝘁𝗵 𝗼𝗳 𝗖𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝗮𝗺𝘀 𝗖𝗼𝘂𝗹𝗱 𝗟𝗲𝗮𝗱 𝘁𝗼 𝗦𝗶𝗹𝗼𝘀 𝗼𝗳 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 For years, manufacturing has battled against data silos, relying on centralized data models and transformation programs. However, these centralized transformation teams are increasingly being scaled down and integrated into individual business units. This shift, combined with the proliferation of data, self-service analytics tools, and no-code/low-code platforms, will enable decentralized initiatives to generate a wealth of insights. But while data is quantitative and objective, insights are subjective and open to interpretation. Different roles and functions may interpret the same data in different ways, potentially leading to conflicting insights and, ironically, new silos. To address this challenge, businesses must focus on building robust knowledge management systems that go beyond data to include both explicit and tacit knowledge and streamline decision-making to ensure alignment across functions. LNS Research #IndustrialAI #IndustrialDataOps #AgenticAI #AnalystPredictions

The promise of AI....Derailed Generative AI has taken industrial companies by storm. Many companies are experimenting with various AI use cases and setting lofty goals for AI-generated savings. Almost all industrial software companies have some form of generative AI chatbot feature. However, the runway is very short for these chatbots to provide value. Their utility is limited to providing single answers to single questions kinds of interaction. There is also the issue of precision and accuracy in what a chatbot tells us. AI, at least the current iterations of it, isn’t really “intelligent” but rather mimics human speech patterns to give the illusion of intelligence. Think of it as a Google search on steroids. Just like a Google search, sometimes it delivers the wrong results. Companies that sign over too much trust to allow AI to run unsupervised do so at their own peril. However, industrial AI use cases envisioned in today’s manufacturing companies suffer from a bigger problem. Defining the one source of truth of a company’s operations. The issue is data quality and contextualization. Practices vary widely, but the state of industrial “Big Data” operations today leaves something to be desired. In many industrial companies, data is siloed and scattered across the operational architecture, and data operations are not consistent. Often, the ROI for data operations is not a direct line of sight but rather a capability that enables other value-adding capabilities, like AI to accelerate decision speed. The ROI is there, not in the data ops, but without the data ops capability, you can't achieve the decision speed result. Essentially, the idea is Garbage in-Garbage out. This means that when conducting analysis and trying to develop insights for better operating conditions, the quality, cleanliness, and contextualization of the data are extremely important. Six Sigma Black Belts have long understood this idea and have taken great pains when organizing their data for analysis to ensure it is clean and contextualized. Otherwise, an analysis of poor-quality data might send the Black Belt off in the wrong direction, trying to optimize the wrong thing, wasting time, or, worse, making performance degrade. With the current infatuation with Industrial AI, some companies are thinking that they can move past the requirements to clean and contextualize data for AI to operate on, that AI is “intelligent” enough to sort the wheat from the chaff. Nothing is further from the truth. The promise of Industrial AI is significant, and more advanced AI technologies are coming to market at the speed of light. However, there is no escaping the need to ensure AI has clean, contextualized data to work with. The work is hard, time-consuming, and costly, but AI is not the panacea for doing that hard work; rather, it is the result of doing that hard work. .

🚨 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