Authors:
Scăunașu Monica-Teodora
and
Mocanu Mariana Ionela
Affiliation:
National University for Science and Technology POLITECHINICA Bucharest, Computer Science Department, Bucharest, Romania
Keyword(s):
Data Science, Decision Support Systems, Institutional Management, Predictive Analytics, Group Decision- Making.
Abstract:
This article explores the integration of data science into Decision Support Systems (DSS) as a transformative framework for institutional management. Using advanced analytics such as Random Forest classifiers, ARIMA models, and optimization algorithms, the research demonstrates how organizations can transition from static decision-making frameworks to adaptive, data-driven systems. Case studies, including IT risk management and group decision-making frameworks, illustrate the practical application and benefits of these methodologies. The study compares the proposed DSS with traditional systems, underscoring the advancements in predictive analytics, resource optimization, and collaborative decision-making. By aligning predictive insights with institutional priorities, the proposed framework fosters operational efficiency, strategic foresight, and inclusivity, setting a new standard for modern management practices.