Integration of Data Science in Institutional Management Decision Support System

Scăunașu Monica-Teodora, Mocanu Mariana Ionela

2025

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.

Paper Citation

in Harvard Style

Monica-Teodora S. and Ionela M. (2025). Integration of Data Science in Institutional Management Decision Support System. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 820-829. DOI: 10.5220/0013352400003929

in Bibtex Style

@conference{iceis25,
author={Scăunașu Monica-Teodora and Mocanu Ionela},
title={Integration of Data Science in Institutional Management Decision Support System},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2025},
pages={820-829},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013352400003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Integration of Data Science in Institutional Management Decision Support System
SN - 978-989-758-749-8
AU - Monica-Teodora S.
AU - Ionela M.
PY - 2025
SP - 820
EP - 829
DO - 10.5220/0013352400003929
PB - SciTePress