Strategies for the Creation and Implementation of Business Intelligence Frameworks

Organizations in the modern era are under increasing pressure from the socioeconomic environment to seek out various technological methods/tools that might encourage the efficient collection, processing, and evaluation of massive volumes of data from disparate and scattered sources to provide guidelines for exploring actual ideas. Thus, this study focuses on how firms use modern instruments in their BI system development processes. This article also suggests the machine intelligence-based demand forecasting system (MI-DFS) for smarter choices in the digital sphere. This method uses big data analytics and a machine learning algorithm to predict the future demand for a product or service. This system gauges the level of acceptability of business intelligence by administering organized questionnaires and interviews with customers and service providers. Using properly classified test data, the results of many experiments show the effect of various forms of machine intelligence. Several learning algorithms were compared to the MI-DFS in terms of efficiency. Recurrent Neural Networks (RNNs) are a supervised learning technique for one-dimensional time series used for demand forecasting. The RNN model achieves the highest F1-measure and prediction accuracy. The proposed model will need to incorporate the financial decision assistance system.