What are the advantages of a data-centric architecture and knowledge graphs?

As products grow increasingly complex due to the integration of hardware, electronics, and software, seamless communication becomes essential for ensuring optimal performance. However, data silos among engineers hinder collaboration, leading to fragmented information, inaccurate analysis, and inefficiencies that negatively impact the customer experience. Without a unified approach, product dependency mapping, intelligent root cause analysis, and end-to-end engineering data integration become significant challenges.

A data-centric architecture and knowledge graphs offer a transformative solution to breaking down these silos. By enabling structured data sharing across engineering teams, these technologies create a single source of truth, allowing for more accurate decision-making and faster issue resolution. Product dependency mapping becomes more intuitive, helping teams visualize complex interrelationships between components, while intelligent root cause analysis accelerates troubleshooting by identifying failure patterns across domains.

Want to learn more? Watch the recording of our semantic technologies introduction, where our Senior Product Owner, Daniel Wilms, takes a deep dive into the two technical pillars powering our Agentic Engineering Intelligence: a data-centric architecture and knowledge graphs - the foundation for unlocking true end-to-end engineering data integration.