Neo4j Closes 2017 With Stellar Corporate, Product And Community Growth As Demand For Graph Databases Skyrockets
SAN MATEO, Calif., Feb. 21, 2018 — Neo4j, the leading platform for connected data, concluded a record year in 2017, surpassing 1,000 customers overall. The company first implemented its commercial open source business model in 2011, and based on 2017 results, has achieved a 90% compound annual growth rate (CAGR) over the next 6 years. Neo4j’s focus on Enterprise accounts (companies over $1B USD in revenues) expanded in 2017, with more than 75% of the company’s current Annual Recurring Revenue (ARR) coming from Enterprise customers.
Neo4j’s growth is pacing the graph database market maturity in the enterprise. In October 2017, Forrester updated their graph databases vendor landscape report, finding that 51 percent of global data and analytics technology decision-makers have implemented, are implementing, are upgrading or are expanding graph databases in their organizations. The report also included a greater number of vendors, an explosion of new graph use cases and the analysis that, “Neo4j continues to dominate the graph database market.”
Neo4j has also shown the impact graph databases can have on global issues of interest. In November 2017, the International Consortium of Investigative Journalists (ICIJ) used Neo4j to analyze the Paradise Papers dataset, which contained more than 13.4 million documents related to offshore tax haven accounts. This, paired with Neo4j’s work with the ICIJ on the Panama Papers in 2016, led to a $1 million grant from the Hollywood Foreign Press Association to fund the ICIJ’s award-winning investigative journalism.
“In 2017, Neo4j continued to show how essential graphs are becoming for organizations of all industries and sizes,” said Emil Eifrem, CEO and co-founder, Neo4j. “From drawing connections within the 13.4 million files from the Paradise Papers leak in November, to powering eBay’s smart personal shopping bot, Neo4j continues to show the true power of graph database technology. Entering 2018, we’re still focused on what got us here — continued technology innovation, making our customers successful and uncovering the best ways for the world to apply and benefit from graph technology.”
Neo4j 2017 highlights and milestones include:
- Neo4j increased its annual recurring revenue by more than 50 percent for the third year in a row.
- Neo4j saw accelerated interest from the financial services sector, signing customers such as State Street Bank and Deutsche Bank, and expanding its footprints in UBS, JP Morgan and Société Générale.
- Neo4j’s enterprise customer count rose to 150, out of 270 non-startup commercial customers. Commonly deployed use cases include master data management, regulatory compliance, anti-money laundering, fraud detection, IoT smart homes, heavy equipment parts assembly, AI-powered knowledge graphs and real-time recommendation engines.
- Neo4j continued its partnership with the ICIJ, including the addition of a Neo4j Connected Data Fellow to the ICIJ team. This dramatically accelerated the analysis of more than 13.4 million documents in the Paradise Papers dataset.
- More than 700 startups use Neo4j’s Startup Program, which provides free access to Neo4j Enterprise Edition for early-stage startups.
About Neo4j, Inc.
Neo4j, Inc. is the graph company behind the #1 platform for connected data. The Neo4j graph platform helps organizations make sense of their data by revealing how people, processes and digital systems are interrelated. This connections-first approach powers intelligent applications tackling challenges such as artificial intelligence, fraud detection, real-time recommendations and master data.
The company boasts the world’s largest dedicated investment in native graph technology, has amassed more than ten million downloads, and has a huge developer community deploying graph applications around the globe. More than 270 commercial customers, including global enterprises like Walmart, Comcast, Cisco, eBay, and UBS use Neo4j to create a competitive advantage from connections in their data.