Graph databases use cases

"Big data" grows bigger every year, but today's enterprise leaders don't only need to manage larger volumes of data, but they critically need to generate insight from their existing data. Businesses need to stop merely collecting data points, and start connecting them. In other words, the relationships between data points matter almost more than the individual points themselves. In order to leverage those data relationships, your organization needs a database technology that stores relationship information as a first-class entity. That technology is a graph database. While traditional relational databases have served the industry well in the past in enabling service and process models that tread upon these complexities, in most deployments they still demand significant overhead and expert levels of administration to adapt to change. Relational databases require cumbersome indexing when faced with the non-hierarchic relationships that are becoming yet more persistent in complex IT ecosystems, with partners and/or suppliers and service providers, as well as more dynamic infrastructures associated with cloud and agile. Unlike relational databases, graph databases are designed to store interconnected data that's not purely hierarchic, make it easier to make sense of that data by not forcing intermediate indexing at every turn, and also making it easier to evolve models of real-world infrastructures, business services, social relationships, or business behaviors that are both fluid and multi-dimensional.