Knowledge graphs are a way to model real-world entities and their interrelations. Unlike data modeling with tables and columns, knowledge graphs are stored in graph-structured databases, where classes are nodes and relationships are edges. This way of structuring data is often more intuitive for business users, is flexible so that data models can evolve over time, and allows new types of analysis with the data you already have.  

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Knowledge graphs allow organizations to create an abstraction layer on top of data that describes how data is connected and where it resides. This results in a clear overview of what data exists and how to retrieve it. The graph helps business users to answer complex data questions without the need to consult their data or domain experts.

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Knowledge graphs integrate heterogenous datasets into a tangible and searchable data model. This data model and approach have proven their value to drive better recommendations, searches, and exploration by business users.