May 29, 2025. Large Language Models (LLMs) augmented with SQL querying and database connectivity capabilities offer a transformative solution to the decades-old problem of data silos in manufacturing.
By Vit Prajzler
Manufacturing companies face a critical challenge that costs them millions in lost efficiency, quality issues, and missed opportunities: data silos.
While Information Technology (IT) systems manage business operations like ERP, CRM, and financial data, Operational Technology (OT) systems control the factory floor through SCADA, MES, and IoT sensors. These two worlds often exist in isolation, creating blind spots that prevent manufacturers from achieving true operational excellence.
Large Language Models (LLMs) augmented with SQL querying and database connectivity capabilities offer a transformative solution to this decades-old problem. By serving as an intelligent bridge between disparate data sources, these AI systems can finally enable the seamless integration that manufacturers have long sought.
The separation between IT and OT systems isn't accidental — it evolved from legitimate security, reliability, and performance concerns. OT networks traditionally operated in air-gapped environments to protect critical production systems from cyber threats. Meanwhile, IT systems focused on business processes, customer relationships, and financial management.
This isolation creates several critical problems. Production managers struggle to correlate equipment performance with supply chain disruptions recorded in ERP systems. Quality engineers can't easily link defect patterns from manufacturing execution systems with customer complaints stored in CRM databases. Financial teams lack real-time visibility into how production efficiency impacts cost accounting models.
Consider an automotive manufacturer experiencing quality issues with a specific component. The root cause analysis requires data from multiple sources: machine sensor readings from the OT network, supplier quality records from the IT procurement system, and customer warranty claims from the CRM platform. Today, this analysis typically involves manual data extraction, Excel spreadsheets, and days or weeks of investigation. By the time insights emerge, production continues and costs accumulate.
Previous attempts to solve data silo problems often involved expensive, complex integration projects. Custom APIs, data warehouses, and extract-transform-load (ETL) processes promised to unite IT and OT data, but these solutions frequently fell short. They required significant upfront investment, months of development time, and ongoing maintenance by specialized technical teams.
Many manufacturers invested heavily in data lakes or centralized analytics platforms, only to discover that business users still couldn't access the insights they needed. Technical barriers, complex query languages, and rigid data models created new silos within the supposed solution. Domain experts who understood the business context lacked the technical skills to extract meaningful insights, while data analysts who could write complex queries lacked the manufacturing knowledge to ask the right questions.
LLMs augmented with SQL querying capabilities represent a fundamentally different approach to the data silo problem. Rather than requiring extensive upfront integration work, these systems can connect to existing databases, historians, data warehouses, and even spreadsheets using their current formats and structures. The LLM serves as an intelligent translator, converting natural language questions into appropriate SQL queries across multiple data sources.
This approach offers several key advantages. Business users can ask questions in plain English without learning query languages or understanding database schemas. The LLM can simultaneously query IT systems like ERP databases and OT systems like historian databases, correlating information that was previously trapped in separate silos. Context from one system can inform queries to another, enabling sophisticated cross-functional analysis that would have been prohibitively complex using traditional methods.
For example, a plant manager could ask: "Show me all equipment downtime events in the past month where we also had supplier delivery delays for the same production line." The LLM would understand this requires querying both the maintenance management system (OT data) and the procurement system (IT data), joining the results based on production line identifiers and time correlations.
Practical applications of LLM-powered data querying in manufacturing are extensive and immediately valuable. Quality management becomes dramatically more effective when defect patterns from production systems can be instantly correlated with raw material batch information from inventory systems and customer complaint data from service platforms. Instead of spending days manually gathering this information, quality engineers can identify root causes in minutes.
Predictive maintenance transforms from a purely OT-focused discipline to a comprehensive business process. Maintenance teams can correlate equipment vibration data and temperature readings with parts inventory levels, maintenance cost histories, and production schedules. This holistic view enables better decision-making about when to perform maintenance, which parts to stock, and how to minimize production impact.
Supply chain optimization benefits enormously from breaking down IT/OT silos. Procurement teams can access real-time production data to better forecast demand, while production planners can incorporate supplier performance metrics and inventory levels into their scheduling decisions. The result is reduced inventory costs, fewer stockouts, and more responsive production planning.
Financial analysis and cost accounting gain unprecedented accuracy when real-time production data feeds directly into business systems. CFOs can understand the true cost implications of quality issues, equipment downtime, and process variations. This visibility enables more informed strategic decisions about capital investments, process improvements, and operational priorities.
Successfully deploying LLM-powered database querying in manufacturing environments requires careful attention to several critical factors. Security remains paramount, particularly when bridging IT and OT networks. Organizations must implement appropriate access controls, data governance policies, and audit trails while maintaining the operational security of production systems.
Data quality and standardization significantly impact the effectiveness of LLM querying. While Datova can work with existing data sources and formats, inconsistent naming conventions, missing data, and quality issues will limit the value of insights generated. Organizations should prioritize data cleaning and standardization efforts in parallel with LLM deployment.
Change management becomes crucial for successful adoption. Manufacturing organizations often have established workflows and reporting processes that have evolved over decades. Introducing AI-powered querying capabilities requires training, process redesign, and cultural change to realize full benefits. Success depends on engaging domain experts, demonstrating clear value, and providing adequate support during the transition.
The maturing of large language models combined with manufacturing's urgent need for data integration creates an unprecedented opportunity. Organizations that successfully implement these capabilities will gain significant competitive advantages through faster decision-making, improved operational efficiency, and better strategic insights.
The technology barriers that once made IT/OT integration prohibitively complex are rapidly falling. Cloud-native LLM platforms can securely connect to on-premises databases, modern APIs simplify system connectivity, and natural language interfaces eliminate the technical expertise requirements that previously limited access to integrated data.
Manufacturing leaders should begin evaluating LLM-powered data analytics solutions now, starting with specific use cases that demonstrate clear business value. Pilot projects focused on quality management, maintenance optimization, or supply chain visibility can prove the technology's value while building organizational confidence and expertise.
The future of manufacturing belongs to organizations that can harness their complete data landscape to drive continuous improvement and innovation. LLMs augmented with SQL querying capabilities provide the key to unlocking this potential, finally delivering on the long-promised vision of truly integrated manufacturing operations.