One of the primary challenges in organizations is the inability to ask new questions about data without human intervention, leading to scalability issues. Many organizations have accepted the limitation that people cannot inquire about their data and rely on hard-coded information meant for general use. This status quo is prevalent due to the historical impossibility of allowing data queries until the advent of large language models. Another significant issue highlighted is the duplication of work within teams, where multiple individuals ask the same questions independently, leading to inefficiencies and time wastage. Moreover, accessing previously asked questions can also pose a scalability challenge, emphasizing the need for improved software solutions beyond large language models.
One of the most promising applications of large language models is giving non-experts the ability to easily query their own data. A potential positive side effect is reducing ad-hoc data analysis requests that often strain data teams.
Sarah Nagy is the Co-founder and CEO at Seek which is using natural language processing to change how teams work with data. She joins the podcast to talk about the platform and providing a natural language interface to databases.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer .
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