Enhanced Framework for Big Data Adaptation Using Hybrid DEMATEL-Based Decision Modeling and Scalable Distributed Analytics

In the era of digital transformation, Big Data adaptation has emerged as a critical capability for organizations striving to leverage massive and diverse data streams for strategic decision-making, operational efficiency, and innovation. Traditional methods for evaluating Big Data adoption—such as the DEMATEL (Decision-Making Trial and Evaluation Laboratory) technique—have provided valuable insights into the cause-and-effect relationships among various influencing factors. However, these methods often fall short in handling uncertainty, dynamic dependencies, and scalability challenges posed by real-world distributed data environments.This paper proposes an enhanced and robust framework for Big Data adaptation that integrates hybrid decision-making models with distributed analytics platforms. The core of our methodology is a fusion of Fuzzy DEMATEL, Analytic Network Process (ANP), and COPRAS (Complex Proportional Assessment), offering a comprehensive multi-criteria decision-making (MCDM) system. Fuzzy DEMATEL accommodates vagueness in expert opinions and quantifies interdependencies among critical adaptation factors—such as Compatibility, Perceived Benefits, Technology Resources, Security & Privacy, and Trialability. ANP and COPRAS enhance prioritization robustness and validate consistency across multiple ranking methods.Furthermore, the framework incorporates Apache Spark to simulate real-time Big Data scenarios, enabling high-speed data ingestion, processing, and analytics. We also introduce Bayesian Networks for probabilistic causal inference, strengthening the understanding of directional relationships among adaptation variables. The experimental setup includes synthetic and semi-real datasets to model enterprise-scale Big Data environments. Comparative results demonstrate that “Compatibility” remains the dominant driver in Big Data adoption across all models, while “Trialability” continues to exert the least influence.To ensure result credibility, we apply validation metrics including the Influence Degree Index (IDI), Entropy Weight Method (EWM), and Consistency Ratio (CR). The results affirm the reliability, accuracy, and scalability of the proposed hybrid approach. A key strength of the framework lies in its ability to integrate subjective expert feedback with objective data-driven simulation, offering a holistic view of Big Data readiness and adoption dynamics.In addition, the paper explores future research directions such as the integration of Edge/Fog Computing using TinyML, Blockchain-based data governance for secure sharing, Explainable AI (XAI) methods like SHAP and LIME to ensure transparency in model decisions, and Federated Learning for privacy-preserving model training across distributed data sources. These innovations not only strengthen the technical foundations of the framework but also ensure its long-term relevance in evolving enterprise ecosystems