Imagine effortlessly querying your business data using plain English. No more wrestling with complex SQL queries or needing specialized database knowledge. That's the promise of ChatBI, a new technology designed to bridge the gap between human language and the intricate world of Business Intelligence (BI) data analysis. Traditionally, accessing insights from BI data required expertise in SQL. But what if you could simply ask, "What were our top-selling products last quarter?" or "Show me the week-over-week change in customer engagement?" ChatBI makes this a reality. This innovative system translates natural language questions into complex SQL queries, opening up data exploration to a much wider audience, from product managers to marketing teams. One of the key challenges in BI is the sheer volume of data and the complexity of the underlying database schemas. BI tables often contain hundreds or even thousands of columns, making it difficult for existing Natural Language to SQL (NL2SQL) tools to effectively link the user's question to the correct data fields. ChatBI tackles this problem by intelligently selecting the most relevant "view" of the data, narrowing down the number of columns and simplifying the query generation process. This view technology, borrowed from the database world, streamlines the process and makes it much more efficient. Another hurdle for NL2SQL systems is handling the nuances of human language, especially in multi-turn conversations. For example, a user might start by asking about sales figures and then follow up with a question about week-over-week comparisons. ChatBI excels at understanding these conversational contexts, ensuring that each follow-up question is interpreted correctly. It uses a specialized matching model to link related questions and maintain the flow of the conversation. Instead of relying on large language models (LLMs) to directly generate SQL, which can be computationally expensive and prone to errors, ChatBI takes a phased approach. It first translates the natural language question into a structured JSON format, which is then used to generate the final SQL query. This method simplifies the task for the LLM, making the process faster and more accurate. Furthermore, ChatBI introduces the concept of "virtual columns" to handle complex calculations and relationships within the data. For instance, metrics like "Daily Active Users" (DAU) often involve intricate formulas. ChatBI stores these formulas as virtual columns, allowing users to access them easily through natural language. ChatBI represents a significant step forward in democratizing data access. By simplifying the querying process, it empowers more people within an organization to explore data, uncover insights, and make data-driven decisions. While the current research is based on a limited dataset, the potential of ChatBI is vast. Future research will focus on expanding the dataset and further refining the system's ability to handle even more complex BI scenarios. The future of data analysis is conversational, and ChatBI is leading the way.