The chapter delves into the comparison between general tools like chat TPT and specialized tools like SEEK for data analysis. It discusses user experience, error tolerance, and handling unstructured data, emphasizing the manual work needed for operational data lakes. The conversation also covers code generation, ambiguity handling in data queries, personalization, governance, and deployment models, highlighting the importance of trust and data protection within teams.
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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