People have seen the value of connected data explode, and it’s a truism that the world’s top financial firms are looking to connect everything – their supply chain, their CRM, their digital and physical assets, their views of their customers, their payment history and so on.
The real problem – and potentially game-changing opportunity – is making the value of those connections increase. There may be an answer in the form of a new way of working with data that is predicated on making the connections in a dataset work for its owner.
That’s in the shape of graph, an approach based on the insight that when it comes to data it’s the relationships that are of interest. They differ from traditional (relational) business databases by specialising in displaying and identifying the relationships between large numbers of data points, and so help organisations make better sense of their data.
Working with graphs is a core ingredient of the success of early internet giants. For example, Google’s success owes much to its ability to rapidly exploit connections in every Web document, optimising the relevance and speed of its searches; likewise, LinkedIn digitally harnesses real-life relationship networks in such a slick way that it has cornered the professional social network market. The graph idea played a part in their path to glory, and is now available as a practical architecture for the wider market.
In a graph database, people don’t have to live with the semantically-limited data model and expensive, unpredictable ways of running queries via joins of the relational world. Graph databases support many named, directed relationships between entities (nodes) that give a richer semantic context. That means the features of the world being modelled can be specified in far greater detail, in order to learn a lot more about what’s going on. Even better, queries are super-fast, since there is no join penalty.
In areas as diverse as retail recommendations, machine learning and shopping chatbots, graph database is proving its power. But it’s also becoming more of a factor in finance, being useful in helping spot financial crimes, preventing and responding to cyber threats, ensuring compliance and being central to data-driven customer engagement strategies. (Read More...)