The real costs of pointing AI at your data warehouse
Blog post from Mixpanel
Layering an AI assistant on top of a data warehouse can appear advantageous, as it combines AI's SQL proficiency with the data already stored, allowing product teams to ask questions in plain English. However, practical issues arise, including high query and ETL costs due to the need for extensive data scans and pipeline management, and AI token costs from repeated attempts at query translation. Furthermore, data governance challenges emerge when teams create inconsistent metric definitions, leading to conflicting AI-generated answers. The lack of real-time data capabilities and the need for persistent dynamic dashboards are additional hurdles, as AI often produces one-off results without consistent metrics. Despite these challenges, the data warehouse remains a critical system of record, but it's not optimized for the specific, repetitive product analytics questions teams require, suggesting the need for an integrated, real-time product intelligence layer.
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