Hybrid Machine Learning and Big Data Analytics Framework for Predictive Business Intelligence and Data-Driven Decision-Making

The business data production has surged significantly due to the use of digital platforms, enterprise applications and transactions being conducted over the Internet resulted in the huge demand for the advanced analytical tools in order to apply predictive business intelligence and the data-driven decision-making. The traditional business intelligence software is not capable of processing large amounts of heterogeneous data, recognizing complex patterns in data, predicting business trends and providing timely insights. In order to solve these problems, this paper suggests a new Hybrid Machine Learning and Big Data Analytics Framework as the Predictive Business Intelligence Tool. The suggested framework combines distributed big data processing, data preprocessing and modeling, feature engineering, machine learning algorithms, and analytical visualization which allows transforming complex business data into the useful insights. The proposed framework delivers a number of functions such as demand forecasting, consumer behavior evaluation, risk forecasting, anomaly detection, and business effectiveness improvement. Its hybrid architecture makes it possible to bring together the scalable data processing technologies and predictive analytics algorithms making the analytics process even more efficient. The proposed framework can be assessed in terms of accuracy, precision, recall, F1 score, root mean squared error among others depending on the type of the task to perform. The framework will allow providing efficient and adaptive analytics processes within a variety of business areas including retail, finance, production, and supply chain management