Top 5 use cases for graph databases

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The graph database is one of the biggest innovations to come out of the NoSQL database boom that rocked the industry more than a decade ago. Graph databases have been developed to learn from huge amounts of interconnected data. They store the relationships between data objects within the objects themselves, allowing extremely fast analysis that is almost impossible to achieve by other means.

Graph databases are intended to work alongside relational databases, which are still the repositories of choice in most companies, rather than replacing them. Their main advantage is the ability to quickly perform complex queries on data from multiple systems without the overhead of table joins or data transformations. The aggregation of this remote data presupposes data integration efforts, often in the form of a data lake.

The benefits of graph databases go beyond simple query speed. Complex relational models no longer need to be built in the usual, arduous way, because relationships can be modeled easily and schemas can change dynamically. Still, those fluent in SQL shouldn't feel left out; Graph database query languages ​​such as GSQL are adjacent languages ​​to SQL supplemented with graph capabilities.

Significantly, the focus on relationships and the ability to efficiently handle large amounts of data make graph databases ideal for artificial intelligence and machine learning (ML) applications. . This combination can be enhanced when the graph database software includes AI/ML-specific tools and interoperability features.

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So what are the new use cases for these new features? Here's how five industries are taking advantage of the lightning-fast relational query performance of graph databases in distributed data stores.

1. Get a 360 degree view of customers

Interactions between businesses and their customers or prospects tend to be complex, with many touchpoints. Ideally, these should result in sales strategies that continuously adapt to customer needs. Such 360-degree scenarios quickly result in many-to-many relationships that, using a relational database, would require laborious modeling and cumbersome table joins to produce actionable insights.

This is the kind of situation where a graph database shines. UnitedHealth Group (UHG), for example, adopted a graphical database to help improve the quality of care for more than 26 million members while reducing costs. UHG, the world's largest healthcare company by revenue, uses a massive graph database to track over 120 billion relationships between members, providers, claims, visits, prescriptions , procedures and more.

UHG has developed various GUI applications on its graph database which, among other benefits, provide a consolidated view of member interactions between physicians, pharmacies, clinical labs, health advisors and UHG itself. More than 23,000 users access the database every day, allowing providers to determine better care and wellness recommendations based on the latest member activity in real time. UHG predicts that cost savings could eventually run into the billions.

2. Transform financial services with AI

The exponential growth of data has been the biggest enabler of AI/ML, which requires vast amounts...