While the formulae for love may vary based on the dating site, they are all underpinned by data. This is where graph databases make a difference, with digital leaders like Google and LinkedIn owing their breakthrough to having adopted this revolutionary approach to data very early on.
Graph databases differ from relational databases – as the vast majority of everyday business databases are called – as they specialise in identifying the relationships between multiple data points. Thus Google was able to exploit the connections in every Web document and build a connected dataset to rank search result, getting back substantially better search results: a fundamental factor in its meteoric rise.
LinkedIn, meanwhile, digitises real-life relationship networks, common business contacts and friends-of-friends in a slick way that has given it total domination of the business social market space. Those relationships can be spotted and followed by the code much more quickly than in a relational database because those ‘relationships’ (in the graph database world, ‘joins’) don’t need to be created in order to run a query. This means much improved query time, supporting speedier transactions. The graph database can also query and display a huge volume of connections between people, preferences, personal profile criteria, and so on.
And although not dealing with two human connections, managers in a very different industry to online dating – retail – should be able to see the potential in matching up prospective customers with their ‘ideal’ products or services.