Machine learning models for evaluating the benefits of business intelligence systems

Abstract

Due to the uncertainty of the market and the intensity of rivalry, business owners and managers are often compelled to experiment with a wide variety of strategies for enhancing their company's performance. By enhancing the timeliness and quality of inputs to the decision-making process, Business Intelligence (BI) is one such idea and tool that combines operational data with analytical tools to show complex and competitive information to planners and decision-makers. Business intelligence (BI) tools help companies rapidly generate insights that guide managers toward operational efficiencies, lead them to new opportunities, and set them apart from the competition. The literature study shows that there is a debate about whether BI tools have an effect on the quality of decisions and the development of businesses. The present research explores the varied empirical facets of BI application through ML models. This study concluded with a discussion of how Machine Learning models can be used to assess the value of BI tools. Machine learning models, fed with historical data and a wealth of input features, can foresee the effect of new systems on metrics like revenue development, customer behavior, and inventory management. Using these models, businesses will be able to better evaluate potential investments in new tools and systems.