Elevating data into actionable intelligence
The transition from raw data to a comprehensive knowledge graph involves integrating machine-generated and non-machine data sources. Tags are applied to establish an asset structure representation as the foundation, while a unified namespace is established to harmonize information from disparate origins. The knowledge graph then evolves through outcome-based layers, creating a holistic and structured knowledge repository.
Unlocking the potential of graph data models in manufacturing
Industrial Knowledge Graph combines data seamlessly, unlocking the potential for superior predictions, insightful inferences, and informed decision-making. This comprehensive solution encompasses integrated graph storage, physics, machine learning, advanced analytics, and dynamic visualization—all backed by enterprise-grade security controls. With these indispensable tools, one can confidently expand operations without limitations, exploring a new era of data-driven excellence.