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"It's just so nice to log in any time of day and have up to the day accurate data. It's been a fun surprise for me just to have a better view into how our product has been selling. And like, men, women, by style, by size, by specific product, that's been a nice unlock for us through Zenlytic."

Paul, the CTO and co-founder of Zenlytics, discusses how LLMs and semantic layers enable self-serve analytics. He explains that self-serve is a spectrum that increases capabilities continuously based on the power of underlying technology. While large language models (LLMs) are powerful tools for understanding intent and distilling it into useful information, they require business context to be able to make correct decisions. This is where semantic layers come in - they encode important information like definitions, dimensions, joins, etc., ensuring correctness every time you calculate something. Companies without proper semantic layers often struggle with ad hoc SQL queries or outdated dashboards which can lead to errors in reporting. Warby Parker is a good example of a company that spends most of its data team's time refining their semantic layer to ensure consistency in metrics across stakeholders.

Tobias Macy interview Paul Blankly and Ryan Janssen about their no-code business intelligence tool, Zenlytic, which aims to enable self-serve BI through conversational technology. They explain that the intersection between large language models and semantic layers is necessary for effective self-serve BI. To achieve this goal, they focus on asking clarifying questions to help end-users articulate what they actually want from the data.