Title: Data Science for Intelligent Decision-Making: An Integrated Analytical Framework, Benchmark Evaluation, and Responsible Deployment Model
Authors: Dr Neha Paliwal
Volume: 10
Issue: 8
Pages: 33-43
Publication Date: 2026/08/28
Abstract:
The expansion of digital technologies has transformed data into a central resource for organizational planning, scientific investigation, engineering optimization, and intelligent decision-making. However, the value of data depends not only on its volume but also on the ability to transform heterogeneous information into reliable and actionable knowledge. This study develops an integrated framework for data science that connects problem formulation, data acquisition, quality assessment, exploratory analysis, feature engineering, predictive modeling, evaluation, responsible deployment, and continuous monitoring. The study synthesizes major research perspectives on data science while introducing a controlled benchmark experiment to demonstrate the proposed methodology. A synthetic dataset containing 5,000 observations was constructed for a binary classification task, and four machine-learning approaches-Logistic Regression, Random Forest, Gradient Boosting, and Multilayer Perceptron-were comparatively evaluated. The experimental benchmark showed that the Multilayer Perceptron achieved the highest predictive performance, with an accuracy of 81.70% and ROC-AUC of 90.28%, whereas Logistic Regression provided the baseline performance with an accuracy of 74.80% and ROC-AUC of 83.52%. The findings demonstrate that nonlinear models can provide improved predictive capability when relationships among variables are complex. Nevertheless, the study argues that predictive performance alone is insufficient for practical deployment. Data quality, privacy, interpretability, fairness, computational efficiency, human oversight, and continuous monitoring should be considered simultaneously. Based on these findings, an integrated five-level data-science maturity model is proposed to support organizations progressing from descriptive reporting toward adaptive intelligent decision systems. Because the experimental dataset is synthetic, the numerical results should be interpreted as a methodological demonstration rather than evidence from a real population. The proposed framework provides a foundation for future validation using real-world datasets across healthcare, manufacturing, agriculture, education, finance, logistics, cybersecurity, and smart-city environments.