graph database example

Examples of Graph Database Use Cases

What are graph databases?

Graph databases are systems that store and organize data points in a graph structure. This structure allows for the formation of relationships between nodes, or data points, as well as easy visualization of data silos and connections between entities. By using this type of database, organizations can gain better insights from their data by understanding the relationships between nodes.

Graph databases also have the advantage of enabling quick access to customer data such as purchase histories and IP addresses, providing a comprehensive view of all data points. Additionally, graph databases allow for the use of a query language to quickly analyze big data sets, making them an invaluable tool for modern businesses.

Why are graph databases important?

Graph technologies are a groundbreaking way to address uses other methods can’t handle efficiently. Graph databases let users capture relationships between data entities, providing a more efficient way to explore connections and draw new conclusions. Algorithms that explore paths and distances between vertices help determine importance, and because graph databases explicitly store relationships, queries, and algorithms can run in sub-seconds rather than hours or days. By using graphs for analysis and machine learning, we can discover more about the world around us.

Examples of graph database use cases

Graph databases are particularly useful for organizations that need to connect and analyze large amounts of data quickly. Here are some popular use cases for graph databases:

Fraud detection

Graphs can model business events and customer data to detect fraudulent behavior. “Entity Link Analysis” is used to identify suspicious links between entities, such as:

  • Names
  • Dates of birth
  • IP addresses
  • Device identifiers
  • Access times

Suspicious links are flagged and investigated by humans and marked “high-risk” for future checks. Automation improves through pattern analysis and generates inputs for a Machine Learning (ML) training set. More data is input into the ML model to flag potential fraud, which is either blocked or flagged for human investigation.

Machine learning

Machine learning is heavily reliant on graph databases in order to find correlations between entities. Graph databases allow machine learning algorithms to identify patterns among large sets of complex data in a much more efficient manner than traditional databases. Graphs are also important for understanding the context of data sets and allowing machines to accurately infer relationships in order to make predictions.

Recommendation engines

Real-time recommendation engines are essential to online business success. Making relevant recommendations requires correlating product, customer, inventory, supplier, logistics, and sentiment data. A real-time engine instantly captures new interests. Graph databases outperform other data stores for gaining insight into customer needs and product trends.

360-Degree customer view

Graph databases are great for integrating data from different parts of a business. This provides insight by creating a unified view of the overall landscape, enabling a “360-degree customer view.” Usually, customer data is scattered across different platforms, like SalesForce, HubSpot, or even MailChimp. To create a 360-degree view, data is streamed into a graph using a common data model. A live graph of all customer data enables rich queries on customers and trends. Marketers can use this for deep dives into customer demographics, aggregating behavior based on marketing events, and more. Initially, marketers can access data through visual query interfaces with live views of customer interactions. As more features are developed, machine learning models can be trained to predict the impact of planned marketing activities on customer engagement.

Network mapping

Representing infrastructure mapping and inventory as a graph is a natural fit, particularly when mapping relationships between connected physical or virtual hardware and the services they support. These tools help track components, their purpose, software versions, and the interdependencies between them, ensuring smooth system operations.


Graph databases have become an invaluable tool for businesses of all sizes, providing insights that would otherwise remain hidden. By enabling the exploration and analysis of relationships between data points, organizations can gain a more comprehensive understanding of their customers and products. The advantages of graph databases make them ideal for fraud detection, machine learning, product recommendations, customer data integration, network mapping and other use cases.

Whether you’re a small business or an enterprise organization, graph databases provide a powerful tool for data exploration and analysis. Lumics offers comprehensive graph database solutions to help businesses make the most of their data. Visit our website to learn more about how we can help your organization unlock the full potential of its big data.