Graph databases aren't new but they have been underutilized—that is, until distributed and operational graphs were introduced recently. At Data Summit Connect 2021, Victor Lee, head of product strategy and developer relations at TigerGraph, drilled down on what graph technology can offer organizations and why they should be using them now.
Graph-powered machine learning and analytics are enabling unprecedented benefits, said Lee, who cited Gartner’s statement that “Graph analysis is possibly the single most effective competitive differentiator for organizations pursuing data-driven operations and decisions after the design of data capture.”
Machine learning enables more and better insights to be uncovered from graph databases. Once graph-based features have been generated, AI can search huge amounts of data faster and more accurately than was possible in the past.
Exploring why graphs and machine learning are a fit, Lee said, the combination enables:
Richer, smarter data
- Connections-as-data
- Connects different datasets, breaks down silos
Deeper, smarter questions
- Look for semantic patterns of relationship
- Enables users to search further and wider more easily and faster than with other databases
More Computational Options
- Graph algorithms
- Graph-enhanced machine learning
Explainable Results
- Semantic data model, queries, and answers
- Visual exploration and results
Looking at why graph technology is important particularly for machine learning, Lee said it provides a natural data model since graph is how we think. Showing connections between entities, graph-based features enable richer data. In addition, he said, graphs have always had a natural role in machine learning and graph data models are uniquely qualified to provide explanatory AI. In addition, native graphs with massively parallel processing like TigerGraph enable large-scale feature extraction and in-graph analytics.
Showcasing how graph databases and machine learning enable real-world advantages, Lee cited graph database use in healthcare with graphs enabling real-time recommendations, improving treatments, and lowering cost. In industrial supply chains, analytics can be accomplished faster to reveal opportunities and optimize tactical and strategic decisions. In financial services, it supports real time fraud detection by delivering real-time visual results for investigators.
Lee’s session was titled “Laying an Analytics Foundation for the Enterprise using Machine Learning and Graphs.”
More information about Data Summit Connect 2021 is available here.
Replays of all Data Summit Connect 2021 sessions will be available to registered attendees for a limited time and many presenters are also making their slide decks available.