NASA, The German Center for Diabetes Research (DZD), TCS, and More Use Neo4j Knowledge Graphs for Next-Level Digital Transformation
Two-Thirds of Neo4j Customers Implement Knowledge Graphs to Redefine What’s Possible in Data Management & Analytics
SAN MATEO, Calif. – September 8, 2021 – Neo4j®, the leader in graph technology, announced a surge in demand for knowledge graphs. Three trends spur this accelerated adoption:
A shift toward automation, driven by AI and machine learning
Context-rich, comprehensive applications that grow with evolving business requirements
Increasingly complex, connected data that’s easily navigated, analyzed, and understood for invaluable insights
The majority of Neo4j customers have implemented knowledge graphs for various use cases, including medical research, skills mapping, sustainability, data analytics, fraud detection, data compliance, machine learning, and more. From bridging data silos and building data fabrics to accelerating machine learning and AI adoption and providing a blueprint for digital twin, knowledge graphs are foundational and allow businesses to go beyond digital transformation.
Caption: Neo4j knowledge graphs are being adopted across a wide variety of use cases and industries to help businesses redefine what’s possible in data management and analytics.
An actioning knowledge graph automates processes for better outcomes by providing data assurance, discovery, and insight. Neo4j customers, such as MANTA and ASTRIAGraph use actioning knowledge graphs to advance critical initiatives.
MANTA’s automated lineage platform allows users to understand how data flows and transforms throughout its journey across all systems. By embedding Neo4j’s graph database, Manta provides its customers with a more holistic and scalable view of their data pipelines, allowing better governance, compliance, migration, and metadata activation.
ASTRIAGraph monitors the Earth’s orbit for space objects, including functioning hardware and abandoned junk, striving for safety, security, and sustainability. Using a Neo4j knowledge graph, they relate and categorize disparate space domain data that locates and tracks objects from the size of a mobile phone to the largest satellite, to predict their trajectory, minimize risk, and provide complete visibility. With the goal of maximizing decision intelligence, ASTRIAGraph both curates information and creates models of the space domain and environment.
A decisioning knowledge graph surfaces data trends to augment analytics, machine learning, and data science initiatives. TCS, NASA, and The German Center for Diabetes Research (DZD) use Neo4j knowledge graphs to drive powerful and efficient decision-making:
NASA: David Meza, R&D Lead People Analytics and Sr. Data Scientist at NASA, emphasizes the importance of understanding the people who make up an organization and how they leverage knowledge graphs to overcome workforce challenges.
“We chose to build a talent mapping database using Neo4j knowledge graphs to show the relationships between people, skills, and work roles at NASA,” said Meza. “Bringing these relationships together helps the organization find its gaps, weaknesses, and strengths, and address overall workforce challenges. Further, it helps identify the data skills required for various projects, whether that’s getting back to the Moon or going to Mars.”
The German Center for Diabetes Research (DZD) network accumulates a huge amount of biomedical data that is very unstructured, heterogenous, and not connected. DZD set out to better understand the causes and prevention of diabetes from various angles. Using a knowledge graph and graph algorithms, the team has launched a master database providing its scientists with quick access to detailed information, helping to address rich, complex questions in the quest to understand treatments and lifestyle interventions for this metabolic disease and its long-term complications.
TCS IP2™ leverages knowledge graphs to help with real-time diagnostics and optimization of power plants. Using AI, ML, IoT and digital twin technologies in conjunction with knowledge graphs, TCS IP2™ can better predict and pre-empt failures, optimize operations, lower fuel consumption, cut emissions, and identify future possibilities, all without heavy investments or hardware changes.
IT analyst firm Gartner has recently stated that:
“A change in thinking and development of a ‘graph mindset’ are taking place as more organizations identify use cases that graph techniques can solve. Up to 50% of Gartner inquiries on the topic of AI involve discussion of the use of graph technology.” Source: Gartner, Top Trends in Data and Analytics for 2021, Rita Sallam et al, 16 Feb 2021.
Dr. Maya Natarajan, Senior Program Director, Knowledge Graphs at Neo4j, emphasized the value that knowledge graphs unlock when applied to next-level digital transformation.
“Digitalization on its own won’t transform your business,” said Dr. Natarajan. “Knowledge graphs drive intelligence into data by representing it in a way that encodes meaning using context and relationships. It’s a unifying approach to find relevant data and, just as importantly, to interpret and act on what’s significant. That means people and systems can reason about the underlying data and use it confidently for complex decision-making. Connected data, enriched with meaning, allows for better answers to complex queries and insights with more efficiency.”
For More Information
To learn more about Neo4j how NASA, TCS, DZD, and others are leveraging knowledge graphs, visit Connections: Knowledge Graphs for Transformation, a previously hosted virtual event on the ways knowledge graphs drive industry disruption and business transformation.
Download your free copy of Knowledge Graphs: Data in Context for Responsive Businesses by O’Reilly Media, and join us on September 14 to hear from the authors on their new book.
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 with 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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