Lessons from Human Data Analysts to Improve the AI Variety
Over the past few weeks, I’ve been exploring the landscape of self-serve analytics and examining why they often fall short of achieving true “data democratization.” I’ve touched on some common UX pitfalls and the technical limitations caused by evolving data models. Now, it’s time to turn our attention to AI—specifically the AI Data Analyst. In the context of self-serve analytics, the “AI Data Analyst” often takes the form of a text-to-SQL chatbot. The idea is simple: a business user types in a data question, and in return, they get a plot and some explanatory text to help them interpret it. Amazing, right? Problem solved. Data teams are freed from disruptive ad-hoc requests, and business teams get instant answers to their pressing questions. We’ve finally reached the promised land of data democratization. 🦗 If only it were that easy. After a decade working on and with data teams, I know what it takes to be successful as a data analyst - and SQL is just one piece of the puzzle. What c
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