Four Common Graph Database Use Cases You Need to Know

A graph database is designed around the concept of a mathematical graph. Unlike relational databases, they allow you to connect data together. This enables users to take advantage of specific functions from within the graph database that help during data analysis. While a graph database stores the same kind of data as any other, it allows you to see how data may be related without having to run a JOIN to understand the relationship. There are a growing number of graph database use cases to be aware of.

The structure of a graph database enables it to map different types of relational and unstructured data. This means that it can provide a view of both simple and complex relationships between seemingly unrelated data. All of these factors mean that graph database users can see the links between data without having to first create a hypothesis about a particular data set and test it.

Graph databases are becoming increasingly popular due to the proliferation of unstructured enterprise data. The structure of a graph database makes it a perfect place to store, manage and link these new data types. Not only do graph databases enable succinct data connectivity, they also provide users with a faster path to  accurate data analytics. With this in mind, our editors have compiled this list of the most common graph database use cases you need to know.

Master data is made up of essential company-wide data points. This data typically provides insight related to the core of the business, including customers, suppliers, accounts, employees, goals, and operations. Decisions about what constitutes as master data are made by management teams and business stakeholders. Once these data standards have been met, users can analyze the data as they need to identify key metrics that reveal areas of concern so appropriate actions can be taken to improve operations.

Since master data consists of a series of connections, managing it using a relational database structure can be both complex and slow. In addition, real-time querying is a daunting task due to the fact that users often need to integrate master data with cross-enterprise applications. Graph databases support the relationships between data, so they offer a more efficient and effective way to organize it. This means more relevant recommendations for those working with master data, and as such, even greater flexibility.

The growing presence of regulations is putting a strain on the enterprise, especially those organizations that store sensitive customer data.