Towards Data Science is a community-powered publication that showcases work in data science, machine learning and artificial intelligence. Every day newcomers, seasoned researchers and industry practitioners publish tutorials, research notes and real-world case studies that help the field move forward.

Towards Data Science

Text-to-SQL systems require near-perfect accuracy in enterprise settings because even 90% accuracy erodes trust and leads to poor business decisions. Building production-ready Text-to-SQL involves complex RAG pipelines with intent classification, vector databases, embeddings, and retrieval mechanisms. Modern evaluation frameworks like Spider 2.0 expose the reality gap between academic benchmarks and enterprise complexity, testing models against massive schemas (800+ columns), multiple SQL dialects, external business knowledge, and agentic workflows. Execution Accuracy (EX) and Soft-F1 metrics provide more meaningful evaluation than simple string matching, while platforms like BigQuery integrate AI capabilities natively but come with vendor lock-in tradeoffs.

Why 90% Accuracy in Text-to-SQL is 100% Useless

BigQuery: A Case Study in Native AI Integration

Conclusion: The Binary Bar for Enterprise Data