NASA, ICIJ, ATPCO, Lyft and More Choose Neo4j for their Knowledge Graphs

Neo4j Sees Surge in Demand for Enterprise Knowledge Graphs; Independent Survey Reveals that 89% of IT Leaders Plan to Expand Their Knowledge Graph Initiatives

SAN MATEO, Calif. – September 23rd, 2020 – Neo4j®, the leader in graph technology, announced a Knowledge Graph Quick Start program to support the company’s rapidly growing knowledge graph customer base. This market acceleration is corroborated in the results of an independent survey, "Technology Executive Priorities for Knowledge Graphs” recently conducted by Pulse, which charts a surge in demand for knowledge graphs among large enterprises.

At their most basic level, knowledge graphs are used to share information, as the basis for data management and governance and as a data fabric. Neo4j customer use cases tend to use knowledge graphs for general analysis, forecasting, what-if scenario planning and increasingly to contextualize AI (artificial intelligence) and machine learning systems. Increasing integration of knowledge graphs into broader business systems is widely expanding their use for process initiation and automation.

Neo4j expects enterprise demand for its knowledge graph technology to grow, an outlook that is backed by the Pulse survey results. The majority of IT decision makers surveyed (89%) have an active plan to expand their knowledge graph initiatives over the next 12 months. Moreover, 92% of respondents believe that knowledge graphs improve machine learning accuracy and associated processes.

An overwhelming majority of technology executives (97%) believe that there’s more potential within their organization for knowledge graph usage. The top three reasons to expand knowledge graphs are to improve machine learning and artificial intelligence systems (60%), open new revenue streams (50%) and connect data silos to make information more accessible (50%).

Caption: Neo4j expects enterprise demand for its knowledge graph technology to snowball. According to IT leaders, the top drivers of expansion will be machine learning, AI, opening new revenue opportunities and connecting data silos.

Several factors contribute to the increase in demand for knowledge graphs. Global stresses from the COVID-19 pandemic to secondary issues such as supply chain disruptions shine a light on rigid, outdated, inefficient systems that are straining organizations and networks to breaking point. Increasingly remote workers require codified, organized information that is often perishable “tribal knowledge.” Applying relevant knowledge is the single most powerful lever a business has to remain nimble, creative and resilient in an uncertain and frenetic market.

Neo4j Knowledge Graph Customer Success

Neo4j knowledge graphs bring complete visibility to data, processes, products, customers and – most importantly – how they all interrelate. As a result, organizations can see the bigger picture and make more informed decisions. The Neo4j native graph database provides a flexible and intuitive representation of real-world complexity, naturally capturing contextual information and unifying fragmented data. The result is relevant, timely data that drives forecasting and actions based on holistic information.

Organizations that have implemented Neo4j knowledge graphs are well-positioned for agility. Here are some examples of how they use Neo4j to advance critical initiatives:

Neo4j is currently being used by the ICIJ for the FinCEN Files. Neo4j has been working with the ICIJ since the 2016 Panama Papers investigation, which has recouped more than $1.2 billion of tax revenue in 22 countries, with tax evasion investigations continuing in more than 82 countries. In 2017, the ICIJ won the Pulitzer Prize for Explanatory Reporting for their work on the Panama Papers.

“Using Neo4j, someone from our Orion project found information from the Apollo project that prevented an issue, saving well over two years of work and one million dollars of taxpayer funds.” David Meza, Chief Knowledge Architect at NASA

“Ninety percent of Lyft data scientists are using Amundsen [knowledge graph using Neo4j] to do their jobs on a weekly basis. We also found that this tool has increased productivity for our entire data science organization by around 30%.” – Tamika Tannis, Software Engineer, Lyft

According to Jussi Vira, CEO of Turku City Data, Neo4j was chosen for their city-wide knowledge graph n-bridges, because the property graph model enables them to continuously write back to their graph and derive better solutions to address key city priorities.

“With Neo4j as a foundation for n-bridges, Turku City Data uses contextual AI to help solve smart city problems as they arise, with the time to the next project decreasing dramatically,” said Vira. “For example, n-bridges allowed the city of Turku to respond quickly to COVID-19 related issues, such as delivering food to elderly citizens who are homebound. Graph techniques determine routes through the city that optimize delivery speed and transportation resources while maintaining unbroken cold chain requirements. This graph-based route optimization aids the planning and management of safe and resource-efficient food distribution.”

Covidgraph.org provides a new way for researchers to analyze publications, genes, proteins and disease. It’s proven to be an invaluable exercise to find treatments and vaccines in the absence of long term clinical trials and minimal peer-reviewed research.

NEORIS HealthCheck enables companies to understand each employee’s well-being and how infection trends will impact them at their specific location. At the same time, employees will be able to check-in and share critical information about their well-being to help their organization provide the necessary help and guidance.

eBay for Google Assistant uses a Neo4j knowledge graph to infer contextual information within a shopping request. eBay engineers knew that deploying a chatbot to their user base required internet scale with a high degree of resiliency and availability, plus predictable responses in milliseconds.

The German Center for Diabetes Research accumulates vast amounts of data distributed across various locations. It has built a master database to provide its 400-strong team of scientists with a holistic view of available information, enabling them to gain valuable insights into the causes and progression of diabetes.

ATPCO has implemented a Neo4j-powered pricing engine for airfare pricing. Neo4j is at the core of at least five of the primary data services offered, from fare management to air travel tax calculation. Getting a competitive price for a plane journey involves a large amount of complex data processing factoring over 100 billion product permutations.

Operationalize Knowledge Graphs Faster

Neo4j’s Knowledge Graph Quick Start service scopes and builds a solution so that enterprises can go from zero to operational knowledge graph in as little as eight weeks. Neo4j experts use pre-built, customizable solution frameworks with proven code, models and components to build a domain-specific knowledge graph. These supported frameworks include data models and ontologies for financial systems, supply chain, privacy compliance, customer, employee and patient information. Services include:

Neo4j installation and configuration
Applying a solution framework and customizing a data model
Transforming data into a semantic format
Data cleansing, the application of model constraints and entity resolution
Applying graph analytics and machine learning to reshape and complete a knowledge graph
Performance testing and tuning to ensure optimal results

More Information

You can download the highlights from the “Technology Executive Priorities for Knowledge Graphs” survey.

To learn more about Neo4j knowledge graphs at NASA and BMO Financial Group, watch this knowledge graph-focused Neo4j Connections event.

Learn more about Neo4j’s Knowledge Graph Quick Start program here.

Resources

About Neo4j

Neo4j is the leader in graph database technology. As the world’s most widely deployed graph database, we help global brands – including Comcast, NASA, UBS and Volvo Cars – to reveal and predict how people, processes and systems are interrelated. Using this relationships-first approach, applications built using Neo4j tackle connected data challenges such as analytics and artificial intelligence, fraud detection, real-time recommendations and knowledge graphs. Find out more at neo4j.com.

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