Natural relationships between data contain a gold mine of insights for business users. Unfortunately, traditional databases have long stored data in ways that break these relationships, hiding what could be valuable insight. Although databases that focus on the relational aspect of data analytics abound, few are as effective at revealing the hidden valuable insights as a graph database.
A graph database is designed from the ground up to help the user understand and extrapolate nuanced insight from large, complex networks of interrelated data. Highly visual graph databases represent discrete data points as “vertices” or “nodes.” The relationships between these vertices are depicted as connections called “edges.” Metadata, or “properties” of vertices and edges, are also stored within the graph database to provide more in-depth knowledge of each object. Traversal allows users to move between all the data points and find the specific insights the user seeks.