Enabling HR Data-Driven Decision Making Through a Natural Language-Based Chatbot for Dynamic Data Visualization

Abstract

This research develops a natural language-based chatbot designed to enhance HR decision-making through dynamic data visualization. Traditional HR data analysis methods are often slow and complex, limiting real-time insights. By integrating natural language processing (NLP) with advanced data visualization, this chatbot enables HR professionals to interact with large datasets using conversational queries, streamlining data access and interpretation. The system translates user input into SQL queries, retrieving data from a data warehouse and presenting it in interactive visualizations. Testing revealed that the chatbot performs well with basic queries, providing accurate results and clear visualizations. However, challenges emerged with more complex queries and multi-layered data visualizations, where accuracy and response time decreased. Despite these challenges, the chatbot’s decision-support capabilities were effective, offering actionable recommendations based on trends and patterns in HR data. While the current system is limited to basic HR tasks, it demonstrates the potential of AI-driven tools in transforming HR processes. Future work will focus on improving query handling, enhancing visualization capabilities, and integrating the system with dashboards for strategic decision-making. Overall, this research contributes to the growing field of AI in HR, showing how NLP can simplify data access and support more informed decision-making.