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Data Skeptic

Graph Databases and AI

Oct 21, 2024
Yuanyuan Tian, Principal Scientist Manager at Microsoft Gray Systems Lab, dives into the world of graph databases and their applications. She discusses overcoming the hurdles small enterprises face in adopting this technology and the importance of the GQL project for standardization. The conversation highlights how graph databases enhance fraud detection in finance, optimize supply chains, and improve healthcare analytics. Yuanyuan also explains the role of large language models and specialized query languages in making these powerful databases more accessible.
35:58

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Quick takeaways

  • Graph databases are increasingly crucial in industries like finance and healthcare for fraud detection and network analysis, highlighting their versatile applications.
  • The establishment of standardized query languages like GQL aims to simplify the integration of graph databases, potentially lowering barriers for smaller organizations.

Deep dives

Challenges in Graph Database Adoption

The graph database industry faces a significant challenge related to its long-tail distribution, where only a few large companies, like Facebook and LinkedIn, use graphs as their core business while the majority of smaller companies are hesitant to adopt graph technology. The complexity of transitioning to graph databases presents barriers, as many organizations lack the familiarity and expertise to handle new query languages and technologies. This leads to frustrations for companies attempting to incorporate graphs into their processes. Current standardization efforts, like the GQL project, are seen as positive steps, but there is still skepticism about whether they sufficiently address the needs of these smaller businesses and the growing trend of leveraging AI tools for data queries.

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