Gaining all round information from Customer Data:
Interactions between companies and their customers or sales prospects tend to be complex, with many touchpoints. Ideally, these should yield sales strategies that continuously adapt to customer needs. Such 360-degree scenarios quickly incur many-to-many relationships that, using a relational database, would require laborious modeling and cumbersome table joins to yield actionable insights.
This is the sort of situation where a graph database shines. UnitedHealth Group (UHG), for example, has adopted a graph database to help improve the quality of care for over 26 million members while reducing costs. The largest healthcare company in the world by revenue, UHG uses a massive graph database to track more than 120 billion relationships among members, providers, claims, visits, prescriptions, procedures and more.
Producing great impact in the Finance Industry:
The exponential growth of data has been the biggest enabler of AI/ML, which requires large quantities of data to surface meaningful patterns and improve the accuracy of decision-making. Few industries are more data-intensive than financial services, but as with other industries, data originates from many different sources and typically ends up in relational database silos.
In bridging those silos, graph databases can help AI/ML deliver superior predictive analytics, risk management, fraud detection, anti-money laundering, insider-trading monitoring, automated recommendations for customers and more. Also, a graph database coupled with AI/ML can help ensure data is clean in the first place, reconciling anomalous differences in customer records and financial product attributes that could lead to inaccurate results.
Improving E-Commerce operations:
Retail ecommerce firms face growing competitive pressure to deliver better customer experiences built on accurate customer details and purchase histories. That foundation enables everything from dynamic pricing to product recommendations to personalized special offers, all of which draw on data accrued along the customer journey.
Graph databases can help in a number of ways. Consider the possible relationships — between customers and payment methods, customers and brands, products and return rates, promotions and sell-through rates, and a whole lot more. Say you wanted to run a query to determine which promotions were most effective for a certain product when pitched to a subset of customers defined as loyal. With a relational database that would take a long time, but a graph database can return the results with very little latency.
Want to extract all the benefits of PostgreSQL along with Graph Database features look at:
Apache AGE
Apache AGE Github
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