Integrating big data and machine learning in management information systems for predictive analytics: A focus on data preprocessing and technological advancements

This article explores the integration of big data and machine learning (ML) within Management Information Systems (MIS) to enable predictive analytics, enhancing real-time organizational decision-making. As businesses accumulate vast amounts of complex data from diverse sources, leveraging predictive analytics in MIS has become critical to gaining actionable insights and maintaining a competitive edge. A core aspect of this integration is advanced data preprocessing, which ensures the quality and usability of large datasets. Effective data preprocessing—through techniques such as data cleansing, transformation, normalization, and reduction—is essential for maintaining data accuracy and relevance, both of which are crucial for predictive model reliability. Technological advancements in data preprocessing algorithms, including natural language processing (NLP) and deep learning, further enhance MIS capabilities by enabling sophisticated analysis of unstructured data and improving model accuracy. These advancements help streamline data handling, reduce processing time, and address issues related to missing or inconsistent data. The article discusses various preprocessing techniques in detail, examining how they optimize predictive analytics by refining data inputs for ML models. Through case studies and examples from sectors like finance, retail, and healthcare, the research highlights the transformative role of big data and ML in MIS, as well as the potential for ongoing advancements to shape future predictive analytics applications. The study concludes by examining future implications, focusing on how continuous improvements in data preprocessing and ML algorithms could revolutionize MIS-driven predictive analytics.