In the last great evolutionary step in databases, we were concerned with the volume, variety, and velocity of data. With an estimated 7.5 septillion gigabytes of data generated daily (that’s seventy-five with twenty-three 0’s behind it), organisations faced challenges simply storing this massive amount of information, let alone making sense of it. To adapt, data models were kept simple – rows, columns, and rigid schemas – and complexity was pushed to the application layer. This was a (partial) mistake.
Gaining insight from large volumes of highly interconnected data is extremely valuable, something to which we are now becoming accustomed. Graph databases, which offer flexible, powerful solutions to manage and analyse highly interconnected data have become a regular part of the enterprise data toolkit. As we look ahead, graph databases are set to play a key role in the future of data management, offering new ways to harness the relationships between data points, providing deeper insights and more efficient operations.
What are graph databases?
Compared to traditional relational databases, graph databases store data quite differently. Instead of storing data in tables and deferring joins to query time, graph databases store data as nodes and edges. Nodes represent entities such as people, products, or events, whilst edges represent the relationships between these entities. This structure complements how we think about real-world data and allows for more natural, efficient queries that focus on the relationships within the data rather than struggling with complex schemas and poorly performing joins.
For modern systems, shifting to graphs is crucial as modern datasets are inherently associative. For example, in a customer relationship management system, it’s important to know not just who the customers are but also how they are connected to their transactions, interactions, and even other customers. Graph databases model these relationships as first class citizens, making it easy to query and analyse them in real time.
Practical applications of graph databases
Graph databases shine in areas where understanding connected patterns and relationships is key to generating insights. Many industries are already using graph technology to solve complex, relationship-driven problems which are impractical at best with traditional data models:
● Fraud detection: Financial institutions use graph databases to track transactions, accounts, and users to spot suspicious patterns; by analysing the relationship between transactions, a graph database can quickly identify unusual activity that may signal fraud. Algorithms such as pathfinding or community detection can be used to reveal hidden connections, making it easier for organisations to stay ahead of increasingly sophisticated fraud schemes.
● Supply chain management: Managing a global supply chain requires visibility into how suppliers, products, logistics, and customers are interconnected. Graph databases can model this complex web of relationships, helping businesses optimise their operations. For instance, by understanding how different suppliers are related to raw materials and finished products, a company can identify potential bottlenecks and inefficiencies in real time, enabling more informed decision-making.
● Customer 360 and master data management: for businesses with vast amounts of customer data, creating a unified view of each patron is essential. Graph databases can integrate data from multiple sources, providing a comprehensive, organised view of customer interactions across various touchpoints. These enable companies to offer personalised services, improve customer experiences, and gain a competitive edge.
Flexibility and agility
Not only are graphs great for analysis, but they also have high flexibility and so are humane to use for demanding projects. In traditional databases, schema changes to accommodate new business entities can be very disruptive, time-consuming, risky and costly. As data changes a great deal you can either deal with these negatives or more commonly let the data hold back your business.
Graph databases, on the other hand, are inherently flexible. New nodes, edges, and properties can be added without requiring major refactoring. Organisations can therefore adapt quickly to changing data needs. For example, in an e-commerce application, adding new relationships between customers, orders, and products can be done with minimal disruptions. This makes graph databases ideal for organisations looking to innovate and scale quickly.
Performance and query efficiency
Another major advantage of graph databases is their ability to perform complex queries efficiently. In relational databases multiple joins are often required to reify some part of the underlying business domain. Joins are one thing that relational databases are poor at, and they can significantly slow down performance, especially as the dataset or number of joins required grows.
Graph databases, by contrast, store relationships natively within the data model. This means that traversing these relationships is much faster and more efficient (and is not a function of the size of the data as it is with relational). Instead of computing joins at runtime, a graph database will traverse edges that were physically stored by a previous write, making it well-suited for queries that involve multiple hops or deep relationships. For example, finding the shortest path between two entities or identifying clusters of related entities is a natural task for a graph database and can be done much faster than in a traditional relational database.
Spotting patterns and gaining insights
The ability to model and query complex relationships in data opens up new possibilities for organisations to gain insight from their data. Graph databases are particularly good at uncovering hidden patterns. For example, in social networks, graph algorithms can identify key influencers by analysing the relationships between users and the products they interact with.
Graph analytics is a powerful tool for discovering patterns and insights within large datasets. By using specialised algorithms, such as community detection, pathfinding, and centrality measures, organisations can better understand the structure and relative importance of parts of their data. This is particularly useful in industries like finance, where identifying patterns in transaction data can help uncover fraud, or in supply chain management, where understanding the dependencies between suppliers and products can help optimise operations.
Graph databases, by contrast, store relationships natively within the data model. This means that traversing these relationships is much faster and more efficient (and is not a function of the size of the data as it is with relational). Instead of computing joins at runtime, a graph database will traverse edges that were physically stored by a previous write, making it well-suited for queries that involve multiple hops or deep relationships. For example, finding the shortest path between two entities or identifying clusters of related entities is a natural task for a graph database and can be done much faster than in a traditional relational database.
Graph databases and the new frontier of AI
One of the most exciting frontiers for graph databases is their integration with artificial intelligence (AI) and machine learning. In particular, knowledge graphs, which represent the connected data within a graph database, are being used to organise and contextualise data for Generative AI applications. Knowledge graphs allow Gen AI systems to access connected data that can improve the accuracy, transparency and relevancy of their outputs.
For instance, in the realm of generative AI, where large language models are used to answer questions or generate content, knowledge graphs provide a layer of structured data that helps the AI system to better understand the context of a query. This reduces ‘hallucinations’, where AI systems generate incorrect answers, and ensures that the AI can trace its answers back to a reliable source.
Moreover, graph databases can be used to train AI models by providing them with rich, contextualised datasets. AI systems can generate more accurate predictions and insights by understanding the relationships between data points. For industries like healthcare this is particularly important, where understanding the relationships between genes, diseases, and treatments can help drive breakthroughs in medical research.
The flexible, powerful and intelligent future of data management
Graph databases represent a major step forward in data management, offering a flexible, powerful, and efficient way to model and analyse the interconnected data that drives modern organisations. Their ability to handle complex relationships, spot patterns, and integrate with AI systems makes them a critical tool for businesses looking to stay ahead in an increasingly data-driven world.
As data continues to grow in size and complexity, graph databases are playing an increasingly important role in helping organisations manage and make sense of it all. From fraud detection to supply chain optimisation, the potential applications of graph databases are vast, and their impact on the future of data management is only beginning to be realised.
Dr. Jim Webber
Jim Webber is Neo4j’s Chief Scientist and Visiting Professor at Newcastle University, UK. Jim leads the Systems Research Group at Neo4j, working on a variety of topics including query languages and runtimes, scale, and fault-tolerance. He has also co-authored several books on graph technology.
