Modeling and Analyzing a Comprehensive Framework of Big Data Value Chain

The rapid growth of big data has introduced significant challenges in storing, processing, and extracting actionable insights from diverse datasets. In this paper, presenting and addressing the problem statements of this paper is how to model and analyses the Big Data Value Chain (BDVC) systematically in order to improve the efficiency and how to use to enable the data-driven of the decision make. We propose an integrated framework that leverages a novel algorithmic approach for data ingestion, storage, processing, cleaning, and analysis. The proposed system combines predictive analytics with state-of-the-art preprocessing techniques to transform raw data into valuable business insights. The framework is validated using three different datasets from the UCI Machine Learning repository, Kaggle, and AWS Public Datasets. Our results demonstrate improvements in data quality and predictive performance, thereby facilitating better compliance with regulatory standards and enhancing overall decision support.