Expect graph database use cases for the enterprise to take off
Major consumer-facing platforms including Amazon, LinkedIn and Netflix run large parts of their data architecture on graph databases. But the technology — which excels at storing the relationships between users, their behaviors and products — has not caught on in more traditional enterprises.
Indeed, only about 2% to 3% of current data processing workloads run on graph databases today, according to Michael Moore, executive director in the advisory services practice at EY. As enterprises take on more analytics projects that need to make sense of the connections between people and products, however, he predicts graph database use cases in the enterprise will rise sharply, accounting for 50% of data processing workloads over the next 10 years.
“I believe all CIOs should be following this graph conversation since it is here to stay,” Moore said in an in an interview at the recent GraphTour San Francisco Conference.
The conversation already includes a comprehensive vendor market. Leading graph database platforms include offerings from Neo4J, DataStax and TigerGraph. AWS, Google and Microsoft also offer native graph database tooling on their cloud platforms.
A type of NoSQL database, graph databases are organized to highlight the connections between entities. A classic application of the technology is a social media network where the database also stores information about whom is connected to whom. The technology improves the speed and precision of analytic models by allowing enterprises to take the entirety of their business data and produce logical connections between the data in a way that links to business functions. Graph databases make it possible to condense much larger data sets to run on an in-memory data fabric. This makes it easier to do sophisticated queries that root out those indirect relationships between business functions for the connections that might improve or reduce profits, efficiency or performance. For example, two individuals who have never made the exact same purchase may nonetheless have buying habits similar enough to improve the product recommendations made to each. Insurance fraud rings using graph database technology for nefarious ends will make the insured party look different from claim to claim, while reusing the same doctors, lawyers or body shops. Fraud analysis engines, in turn, use graph database technology to uncover such patterns.
EY has been working with enterprises on graph database use cases that involve data discovery, data validation, fraud detection, supply chain analytics, recommendation engines, anti-money laundering and for providing a 360-degree view of the customer. In addition to the features described, graph tools can tie things that don’t exist together anywhere else in the system, creating a system of record, Moore said. “Typically, enterprises deploy graphs on large data lakes and use it as a unifying data layer,” he said. Because the data can be structured to mirror business processes, graph database technology can also streamline the conversations between developers and business owners, since the data is organized around metrics the business finds familiar. Graph tools also make it easier to refactor data for new applications that call for new business metrics. In traditional databases, business metrics often need to be computed by joining rows from database tables that were organized to optimize the speed with which data is written into the database (not the connections).
