Toward AI Standards: Graph Technology as a Fabric for Context

Graph technology is a natural fit for AI and machine learning projects across any industry, as it represents connected data and analyzes relationships in a way that treats the connections between data equally important as the data itself. Graph technologies store and use data by showing how each individual entity connects with or is related to others, acting as a fabric for our data imbuing it with context and connections. This approach enriches data making it more useful and meaningful, similar to how stars become more significant when part of a constellation. Graph technologies were originally custom-built for companies like Facebook and Google but have since been developed into graph databases that allow organizations to quickly traverse millions of data connections per second. The technology is now used across multiple industries including government, financial services, healthcare, retail, manufacturing, and more, with applications such as fraud detection, cybersecurity, real-time recommendations, network & IT operations, master data management, and customer 360. Graph technologies have also been recognized as a major leap forward in machine learning, enabling better accuracy, flexibility, and explainability in predictive models and decision-making processes.

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