Title: Data Science-Driven Decision Intelligence: A Multi-Domain Framework for Predictive Analytics, Automation, and Responsible Digital Transformation
Authors: Dr Neha Paliwal
Volume: 10
Issue: 8
Pages: 63-107
Publication Date: 2026/08/28
Abstract:
The rapid expansion of digital technologies has transformed data from a passive organizational resource into an important component of strategic decision-making. Data science now combines statistical analysis, machine learning, artificial intelligence, data engineering, visualization, and domain knowledge to convert complex datasets into actionable information. The earlier source article identifies broad applications of data science across healthcare, education, government, finance, e-commerce, astronomy, bioinformatics, and enterprise resource planning (ERP). This research develops a new framework that extends this perspective from conventional data analysis toward decision intelligence, in which predictive insights, automation, human judgement, and responsible-AI mechanisms operate together. The study proposes a multidimensional framework consisting of six capabilities: data quality, predictive analytics, real-time intelligence, automation, explainability, and governance. A simulated comparative dataset is designed to demonstrate how these capabilities can influence decision effectiveness across seven application domains. The proposed analytical structure evaluates decision speed, predictive reliability, operational efficiency, user confidence, and governance readiness. The study does not present simulated observations as real-world survey evidence; instead, the numerical dataset is explicitly constructed as an illustrative research model for testing the proposed framework. Recent research increasingly emphasizes the transition from descriptive business intelligence toward predictive and proactive decision support, while also identifying data quality, transparency, bias, and governance as important limitations. (ScienceDirect) Research on trustworthy AI further indicates that decision-support systems require interpretability and alignment with domain knowledge rather than relying exclusively on opaque predictive models. (ScienceDirect). The proposed framework therefore positions data science not merely as a technique for discovering patterns but as an integrated mechanism for improving the quality, speed, transparency, and accountability of organizational decisions. The article contributes a new conceptual model, comparative indicators, synthetic analytical framework, and proposed measurement structure that can subsequently be tested using primary or organizational datasets.