Using deep learning to enhance business intelligence in organizational management - Sina Gholami; Erfan Zarafshan; Reza Sheikh; Shib Sankar Sana (Data Science in Finance and Economics, 2023)
Business intelligence (BI) is crucial in organizational management, providing insights that enable informed decision-making. Traditional BI approaches, however, are limited in handling the vast amounts of data generated by organizations today. Deep learning, a subfield of machine learning, has shown great potential in improving BI through automated analysis of complex and large data sets. In this study, we explore the effectiveness of deep learning in enhancing BI for organizational management. We evaluate the accuracy and F-score of our proposed deep learning model against traditional BI methods in a real-world scenario. Our dataset contains a large volume of unstructured text data from customer feedback forms, which presents significant challenges for traditional BI approaches. Our deep learning model is trained using a convolution neural network (CNN) architecture to classify customer feedback into positive and negative sentiment categories. The model achieved an accuracy of 88% and an F-score of 0.86, outperforming traditional BI methods such as rule-based systems and sentiment analysis algorithms. Furthermore, our model's ability to handle unstructured data highlights its potential for processing diverse data types beyond structured data, commonly used in traditional BI.