DataHour: Natural Language to SQL Translation: The Challenges, Evolution and Future

The webinar delves into the complex realm of translating natural language queries into SQL, a critical interface for enabling intuitive access to databases. We begin by defining the problem of Natural Language to SQL (NL2SQL) translation, emphasizing its importance in bridging the gap between non-technical users and relational databases. The challenge lies not only in understanding the nuances of human language but also in accurately mapping this understanding to the structured world of SQL queries while keeping in mind the context and vocabulary used in an organization.

The intricacies of the NL2SQL problem stem from the inherent ambiguity of natural language, the diversity of database schemas, the precision required in SQL query formulation, and the unique vocabulary that might be used in any organization. Various techniques have been explored to tackle these challenges, ranging from rule-based methods and traditional machine learning approaches to deep learning and neural networks, and more recently, LLMs.

Anand Ranganathan is a co-founder and the CTO at OneByZero, a consulting firm specializing in delivering production-grade data & AI solutions. Previously, he was Chief AI Officer at Unscrambl, where he led product development around natural language processing, automated insights, data story-telling, real-time decision-making, and marketing optimization. Before joining Unscrambl, he was a Global Technical Ambassador, Master Inventor, and Research Scientist at IBM. He received his PhD in Computer Science from UIUC and his BTech from the IIT-Madras. He also has over 70 academic journal and conference publications and 30 patent filings in his name.