A frequent issue in data-rich organizations is the difficulty in capturing essential context about the data, which becomes even more challenging to maintain over time. As data utilization widens across these organizations and data models grow in complexity, locating the appropriate dataset can be time-consuming and cause delays.
Select Star is a data discovery and metadata platform that creates a constantly updated knowledge graph of an organization’s data by examining both its structure and actual usage. It provides data with context like popularity, lineage, and semantic models, helping AI and teams to easily find, trust, and utilize the right data. These enhanced metadata layers are also very useful for large language models, greatly enhancing the accuracy of generated SQL queries.
Shinji Kim is the founder and CEO of Select Star, and she joins Sean Falconer to talk about addressing metadata curation challenges, managing data context at scale, employing LLMs for SQL generation, emerging patterns in metadata management, and more.
Full Disclosure: This episode is sponsored by Select Star.
Sean has been an academic, startup founder, and Googler. He has published works on diverse topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent, focusing on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
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Sponsorship inquiries: [email protected]
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