The concept of data science has evolved over time, from individuals in marketing and security organizations analyzing data to answer questions, to the emergence of data scientists as a distinct role. As big data grew, so did the sophistication of tooling for data analysis, leading to increased technical complexity that bifurcated subject matter expertise and technical expertise.

This led to a gap between decision-makers and the data they needed, with requests often requiring clarification, iteration, and follow-up. The need for intermediaries like data teams or AI-powered tools arose to facilitate rapid and intuitive Q&A loops. However, this created additional complexity, as seen in companies like Palantir that attempted to bridge the gap.

The recent emergence of AI-powered tools has the potential to undo this bifurcation by enabling non-technical subject matter experts to interact with enterprise data naturally. This could lead to a future where everybody is empowered to make decisions based on their own analysis, eliminating the need for dedicated data scientists.