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Optimizing Decision-Making: Data Analytics Applications in Management Information Systems Gantari, Lumi; Qurotulain, Olivia; Nuche, Asher; Sy, Omar; Erica, Archa
APTISI Transactions on Management (ATM) Vol 8 No 2 (2024): ATM (APTISI Transactions on Management: May)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v8i2.2202

Abstract

This study delves into integrating data analytics applications within Management Information Systems (MIS), exploring their impact on decision-making processes in organizational settings. The discussion synthesizes qualitative and quantitative methodologies, presenting insights from scholarly literature, surveys, and interviews. Scholarly discourse highlights the transformative potential of data analytics tools in facilitating informed decision-making, aligning with practical applications showcased in empirical studies. However, inherent challenges surface, primarily concerning data quality, as revealed by 62\% of respondents, underscoring the need for organizations to address these obstacles. Despite challenges, substantial adoption rates of data analytics tools (78\%) affirm their growing recognition in decision-making within diverse industries. Reported enhancements in operational efficiency (35\%) and competitive advantage (22\%) among organizations leveraging data analytics validate their efficacy in driving organizational performance metrics within MIS. Further research should address ethical implications, longitudinal analyses of data analytics efficacy, and interdisciplinary collaborations exploring the nexus between data analytics and managerial decision-making. This study is a foundational step, providing empirical evidence and future research trajectories essential for organizations aiming to optimize decision-making through data analytics applications within Management Information Systems.
Big Data Analytics: Transforming Business Intelligence and Decision Making Usino, Wendi; Ayu Rini Kusumawardhani, Dhiyah; Ramadhan, Tarisya; Pratiangga, Aptanta; Qurotulain, Olivia
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/2wf1s376

Abstract

In today's rapidly evolving digital landscape, organizations are increasingly leveraging Big Data Analytics to transform business intelligence and enhance decision-making processes. This study explores how businesses utilize Big Data to gain insights into operations, customer behaviors, and market trends, specifically focusing on the retail, healthcare, and financial sectors. By employing a mixed-method approach that combines qualitative and quantitative data, the research analyzes case studies from a major international retailer, a leading healthcare provider, and a global bank. Data sources include semi-structured interviews with industry experts, surveys, and secondary data from existing literature. The findings indicate significant improvements in customer retention (20\%), operational efficiency (with a 15\% reduction in inventory costs in retail and a 10\% reduction in hospitalization rates in healthcare), and fraud reduction (a 25\% decrease in fraudulent transactions in financial services). However, the study also identifies ongoing challenges such as data quality issues, high implementation costs, and complexities in integrating Big Data Analytics with existing systems. The research concludes by emphasizing the importance of addressing these challenges to fully capitalize on Big Data's potential for competitive advantage and suggests that future studies should explore the ethical implications and the impact of emerging technologies on Big Data Analytics to further enhance its effectiveness in business intelligence.