• Over reliance on the model itself : Treating AI as a black box rather than a tool. • No feedback loop : Without continuous improvement, models quickly become outdated. When these challenges persist, even the most sophisticated AI initiatives struggle to demonstrate ROI. The solution begins with creating a healthy, interconnected data environment that unites people, processes, and technology. What a healthy AI data ecosystem looks like The role of the subject matter expert (SME) A truly effective data ecosystem recognises that people (not algorithms) drive industrial transformation. These SMEs become the bridge between data science and plant operations. Their expertise defines which problems are worth solving and ensures that AI models align with operational realities. SMEs validate outputs, interpret anomalies, and help turn insights into action. In this way, they make AI a partnership between human experience and digital intelligence. The best-performing plants empower their SMEs with accessible, high-quality data and intuitive tools that make exploration and validation simple. What makes a data ecosystem ready for AI To achieve that level of readiness, your data ecosystem must be reliable, centralised, contextualised, accessible, and integrated. Let us break those elements down. • Reliable data historian: At the heart of any AI-ready ecosystem is a high-performance data historian capable of collecting, storing, and managing real-time process data with complete integrity. A robust historian maintains continuous data collection using store-and-forward technology, ensuring no data is lost even during network interruptions. Long-term retention preserves granularity for historical analysis and model retraining. The result is a trustworthy foundation upon which AI systems can operate confidently. • Centralised and unified data sources : Industrial facilities often generate data from disparate systems, including laboratory information management (LIMS), manufacturing execution systems (MES), SCADA, DCS, and more. When this information is fragmented, insights remain limited. By consolidating data from these systems into a single, unified environment, you eliminate silos and create a single source of truth. Time-aligning data streams ensures consistency and enables cross-system analytics that reveal deeper patterns,
such as correlations between lab results, process parameters, and equipment performance. • Contextualised data : Raw numbers mean little without context. Consistent, asset-based tag naming conventions give data meaning and make it easier for users to navigate and compare assets. Metadata such as units, locations, and process identifiers help analysts quickly interpret trends. A clear tag hierarchy that mirrors the structure of your physical plant allows teams to move seamlessly from the big picture to detailed analysis. This contextual layer transforms data into information. • Data accessibility and governance : Data must be both secure and usable. Effective governance ensures that the right people have the right access, with role-based controls protecting sensitive information while still enabling collaboration. Audit trails and version control preserve traceability, helping teams maintain confidence in their data. When users can easily find, visualise, and extract relevant information, adoption accelerates and so does the return on your digital investments. • Integration with AI systems : Finally, your architecture must be flexible enough to integrate with AI tools, not just today but also in the future. Open protocols such as OPC UA, REST APIs, and SQL interfaces allow seamless data flow to third- party platforms. When integration is simple, IT overhead decreases, scalability improves, and your plant remains ready for emerging technologies. This interoperability ensures your investment continues to pay dividends as the AI landscape evolves. dataPARC: the core component of your data ecosystem dataPARC is a comprehensive solution for manufacturing process optimisation that allows process manufacturers to improve efficiency, productivity, and asset reliability. dataPARC tools enable industrial AI technology to perform better by collecting and connecting real-time process data from across the plant, allowing users from the plant floor up to senior management to analyse and extract valuable insights that lead to better results. manufacturers a complete, real-time view of their processes. High-speed collectors interface with hundreds of OPC and custom servers to gather data from your automation layer, while store-and- forward technology ensures nothing is lost during transmission interruptions. High-performance data historian The dataPARC historian is designed to give
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