Graph analytics is a type of data analysis that focuses on relationships between entities rather than just the entities themselves. Instead of tables with rows and columns, graph analytics models data as nodes (entities) and edges (relationships). This approach is especially powerful when understanding connections, influence, and network behavior is more important than simple aggregation.

Examples of nodes include customers, products, users, devices, or accounts. Edges represent relationships such as “purchased,” “follows,” “connected to,” or “transferred funds to.”

Graph analytics is commonly used for:

• Fraud detection

• Social network analysis

• Recommendation systems

• Supply chain dependency analysis

• Identity resolution

• Knowledge graphs

In BI contexts, graph analytics answers questions that are difficult to solve with traditional SQL, such as:

• Which users are most influential in a network?

• How are customers indirectly connected?

• Where are hidden clusters or communities?

• How does an issue propagate across systems?

Technically, graph analytics relies on specialized databases and engines such as Neo4j, Amazon Neptune, TigerGraph, or graph extensions in modern warehouses. Common graph algorithms include:

• PageRank

• Shortest path

• Centrality measures

• Community detection

• Link prediction

From a business perspective, graph analytics uncovers hidden relationships that drive behavior. For example, fraud rings often involve indirect connections that are invisible in tabular analysis.

Graph analytics is increasingly combined with BI dashboards, where graph results are visualized alongside traditional metrics. This hybrid approach gives organizations deeper insight into complex systems.