ADVANCING DATA ACCESSIBILITY: A STUDY ON NATURAL LANGUAGE INTERFACES TO DATABASES

In today's technology-driven world, databases are everywhere -they power everything from business systems to mobile apps.But interacting with them still requires knowledge of SQL, a technical language that many everyday users don't understand.This creates a barrier for people who need data but lack programming skills.Natural Language Interfaces to Databases (NLIDBs) aim to break that barrier by allowing users to simply ask questions in plain language-like "What were last month's sales?"-andget accurate answers from the database without writing a single line of SQL.This paper explores how NLIDBs work, focusing on the role of Natural Language Processing (NLP) in turning natural language questions into structured SQL queries.We look at the evolution of these systems, from early rule-based models to modern solutions powered by advanced machine learning and large language models like GPT.We also break down the technical building blocks-how systems understand user intent, interpret database structure, and ensure results make sense.Beyond the technical side, we also look at real-world benefits and challenges.NLIDBs offer more accessible, user-friendly ways to work with data, especially for non-technical users in fields like healthcare, education, and business.Still, they face hurdles like handling vague questions, adapting to different database designs, and maintaining privacy when using external APIs.Finally, we reflect on where this technology is headed and how future systems could become even more natural, accurate, and widely usable-making it easier for anyone to find answers in data, no matter their background.