0aaEmil Eifrem NeoTechnology

Swipe right to meet your algorithmically matched one true love? Online dating is an online marketer’s success story, generating an estimated $2 billion in revenue each year.

From Match.com to eHarmony, or Tinder, The League and Bumble, the market leaders and new app competitors make those erotic arrows fly based on their savvy use of technology — in particular, for a significant number, graph database technology to help members find the best matches.

However, the love is spreading — as it turns out all sorts of organizations, across all sorts of industries, are building powerful data engines to offer powerful personalized offerings in their markets, finding whole new use cases for smart matches.

Online dating sites are great at what they do because they are so skilled at manipulating large sets of connected data so they can bring similar-minded individuals together, at scale. Other industries can and are following suit — that’s to say, making data work harder for them and for their customers so they can offer customers a compelling offer they ‘fall in love’ with.

The key is recommendations. All online dating businesses are underpinned by personalized recommendations, with the most accurate and successful using graph database technology to manage those algorithms. Graph databases differ from traditional (relational) business databases in that they specialize in identifying the relationships between very large numbers of data points, which help users work with data better.

More and more companies are recognizing the value of those data connections and using graph technology to mine them. Forrester has reported that over a quarter of enterprises will be using graph databases by 2017, while Gartner predicts that over 70% of leading companies will be piloting a graph database by 2018.

Relationships And Connections

Significantly, graph databases are a core technology platform of the Internet giants that premiered recommendations technology, like Amazon and Netflix. Amazon’s success owes much to its ability to rapidly exploit connections between people and product, and offer “Other people also bought”…