Speaker: Walter Ravelo, AVEVA

Abstract: 

Industrial operators and pipeline teams are surrounded by data—but still lose time hunting for the right context across historians, alarms, predictive analytics, engineering tools, documents, maintenance systems, and shift logs. In this session, we introduce a new approach to data aggregation and visualization that uses user centered design and a cognitive walkthrough methodology to align the experience to how frontline users actually think and act. At the foundation is an Industrial Data Knowledge Graph that captures, documents, and governs relationships across systems and records, connecting data ingestion to data consumption to enable secure digital twins and GenAI. We’ll demonstrate the approach through an equipment troubleshooting workflow: a cross application health score and exceptions summary for rapid triage, immediate access to criticality and nameplate data, progressive deep dive context from historian signals, alarms, engineering calculations, and predictive analytics, drill through to source systems when needed, and inclusion of traditionally stranded data like shift logs, construction/engineering documents, and condition based monitoring results—augmented by an Industrial Assistant that synthesizes findings and recommends mitigating actions. While we’ll focus on troubleshooting, the same visualization pattern can be applied.