Lowering the barrier to entry for data analysts
Traditionally, integrating LLMs into SQL workflows for AI-based reasoning of data has been a time-consuming, tedious, and costly affair as it requires data movement, prompt engineering, manual model selection, and parameter tuning, analysts pointed out.
The movement of data is typically required due to SQL’s inability to understand nuance and meaning of unstructured data, making advanced analysis, such as sentiment analysis or categorization, of customer reviews, support tickets, reports, etc., difficult, said Bradley Shimmin, lead of the data, analytics, and infrastructure practice at The Futurum Group.
To bypass this challenge, data analysts often had to export data from the warehouse, send it to a data scientist, and await the data scientist to send back enhanced, categorized data suitable for analysis using SQL, Shimmin noted, adding that the new AI functions “can literally collapse that entire workflow into a single query, using standard SQL syntax.”
