The Three Main Use Cases for a Graph Database
You can’t leverage what you don’t know. Since graph databases enable you to uncover complex relationships between disparate data sets, the technology helps you see things you otherwise wouldn’t know existed. Once those hidden relationships are identified, you can analyze them to inform business processes and decisions.
That sounds nice and all. But how, specifically, can you use graph data to improve your operations and ultimately your bottom line?
Here are three foundational use cases for graph databases.
1. Customer 360
Serving up the best experiences and maximizing the lifetime value of customers starts with understanding each of their behaviors as they move across channels.
Companies who deliver solid customer experiences start by building a Customer 360 application. In the age of social media and mobile proliferation, that’s often easier said than done.
Graph databases can save the day here, too. They can be used to get a holistic, 360-degree view of your customer's data. Customer 360 applications unify your data sources to help anticipate what your customers need ahead of time, all the while delivering a more satisfying experience along the way.
Building upon your C360 application, you can understand customer behavior to create personalized experiences. We see this pattern of development often with our retail customers. Graph databases can help companies that sell things online earn more revenue by delivering real-time personalized recommendations based on customer behaviors.
Remember, the right graph solution will be highly scalable. During periods of high traffic and concurrent use, the technology can still provide personalized recommendations to each customer in a fast, reliable, and highly available manner.
Ultimately, a graph database is your ticket to improved relationships—with your data, your security, and your customers. Security and deep customer relationships mean increased revenue, and the relatively small investment you’ve made in your database will be paying off in spades.
Fraud is a major problem for large organizations. On a global level, fraud accounts for billions of dollars in collective losses each year. Who can afford it?
In the age of data breaches, you might think that organizations have ramped up their security safeguards in an effort to reduce fraud as much as possible. To a certain extent, that’s true.
But, believe it or not, fraudulent activity has actually increased in recent years. A 2018 Experian report, for example, found that 65% of organizations have detected the same or more fraud over the last 12 months.
By comparing real-time transactions within your C360 application to historical behavior learned from personalization, graph data can help detect anomalies and patterns immediately. Enterprises can leverage deeply connected anomalies to reduce or eliminate fraudulent activity before the behavior spirals out of control.
Ultimately, this is the goal of integrating graph databases into your application stack. Our successful customers start by building a solid foundation. Typically, a solid foundation starts with a customer 360 application. Then, you can leverage the unified data to learn your customer’s unique, personalized behaviors. Finally, you can understand what fraud looks like within your customers’ data to prevent huge losses and extremely damaging reputational issues.
In the end, that’s the power of graph: leveraging relationships to help save your company money—and money saved is money made.