Major consumer-facing platforms including Amazon, LinkedIn and Netflix run large parts of their data architecture on graph databases. But the technology — which excels at storing the relationships between users, their behaviors and products — has not caught on in more traditional enterprises.

Indeed, only about 2% to 3% of current data processing workloads run on graph databases today, according to Michael Moore, executive director in the advisory services practice at EY. As enterprises take on more analytics projects that need to make sense of the connections between people and products, however, he predicts graph database use cases in the enterprise will rise sharply, accounting for 50% of data processing workloads over the next 10 years.

Emerging graph database use case: Improving AI

Enterprises are starting to explore using graph databases to improve AI models.

Amy Hodler, graph analytics and AI program manager at Neo4j, said early use cases involve improving the way data is ingested into the AI training tools in a process called feature engineering. For example, researchers at the University of California, San Francisco, have developed, a tool that structures biomedical information to highlight connections. The approach is being used to better correlate genes with disease and predict new uses for existing drugs.

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