Industrial companies are sitting on a goldmine of data - but much of it is locked away in PDFs, legacy drawings, and unstructured documents. What if that data could be transformed into intelligent, connected information that fuels digital twins, simulation, and AI?
In this episode of the Data-driven Engineering, we dive into the challenge of turning decades of legacy engineering material into structured, semantic data that both humans and systems can truly understand.
Mikko Yllikäinen (VP, Product Creation at Cadmatic) is joined by Antti Villberg from Semantum to discuss:
- Why so much valuable engineering data gets “lost” in document handovers
- The role of semantic data models and knowledge graphs in making information machine-readable
- How legacy P&IDs, PDFs, and even laser scans can be converted into intelligent models
- Real-world examples
- How standards like ISO 15926 and OPC UA enable interoperability and plug-and-play integration
- Why structured data is the foundation for successful AI in industrial environments
The key takeaway? Data alone isn’t enough. When data becomes meaningful, connected, and standardized, it unlocks innovation, smarter collaboration, and entirely new possibilities for the future of engineering.