Hear that digital whisper of Cupid’s Arrow? The online dating is a huge success story, estimated to be a £225m business by 2019 in the UK alone, according to Minitel.
From match.com to eHarmony, from Tinder to Zoosk, all the market leaders make those erotic arrows fly based on their smart 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 organisations, across all sorts of industries, are building powerful data engines to offer powerful personalised 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 personalised 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 specialise in identifying the relationships between very large numbers of data points, and so help users work with data better.
Relationships and connections
Significantly, graph databases are a core technology platform of the Internet giants who 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” recommendations. Likewise, Netflix digitally harnesses people and content together in such a slick way that it has cornered the broadcasting media market.
The reason why graphs are the secret heart of these social web giants is that graph databases give equal prominence to storing the data (customers, products) and the relationships between them (who bought what, who likes whom, which purchase happened first). In a graph database, we don’t have to live with the semantically limited data model and expensive, unpredictable joins available in the SQL/relational world. By contrast, graph databases support many named, directed relationships between entities or nodes that give you a rich semantic context for the data. Developers can also incorporate new data sources, use the most recent transaction data and interoperate with existing transactional systems; while relational databases cannot flex in this way.
Put all that together, and you can specify in far greater detail what you want as a customer, but also learn a lot more about that customer as the supplier. Even better, queries are in real-time or near real-time, since there is no join penalty; graphs regularly traverse many levels deep of relationship while delivering real-time performance.
The ‘I’ll know it when I see it’ customer problem
All this makes graph databases especially suited to formulating recommendations, and it’s why they have the potential to transform all kinds of businesses like the online dating world (and the rise of the consumer Web giants before them). Effective product recommendation algorithms have become the new standard in online retail — directly affecting revenue streams and the shopping experience. In parallel, routing recommendations allows companies to save money on routing and delivery, and provide better and faster service.
The business cases just build. According to online UK-based online art marketplace Artfinder: “We have 300,000 unique artworks, so it’s impossible to see all of them; most users can’t articulate what they are looking for – ‘I know it when I see it’.” Remind you of anything? How about searching for love in cyberspace?
The race is on to use this technology in ever more creative ways. Even Amazon, which taught us the value of personalised recommendations, may soon need to play catch-up here, if rivals offer ever-better, personalised recommendations through more intense use of multi-layered, graph-powered data analysis.
The good news is that while consumer Web pioneers had to build their own in-house graph data stores from scratch, off-the-shelf graph databases are now available to any business. That means anyone wanting to use real-time recommendations to influence and get closer to their customers can do just that.
So don’t be coy. Graph databases are not just for the lovelorn any more.
