Topic modelling is a popular technique in natural language processing for identifying latent topics in a collection of documents. In recent years, this technique has gained prominence in the field of business intelligence for extracting insights from large volumes of textual data. Non-negative matrix factorization (NMF) is a widely used method for topic modelling due to its ability to generate interpretable topics. In this research paper, we explore the application of NMF for topic modelling in the context of business intelligence. We conduct experiments on a real-world dataset and evaluate the performance of the NMF algorithm using various metrics. Our results demonstrate the effectiveness of NMF in identifying meaningful topics from textual data, which can be used for various business intelligence tasks such as trend analysis, sentiment analysis, and customer segmentation.