A REVIEW OF DATA-DRIVEN DECISION MAKING IN ENGINEERING MANAGEMENT

Abstract

This research review article explores the transformative impact of data-driven decision-making (DDDM) across various sectors, highlighting the integration of advanced analytics to enhance organizational efficiency, innovation, and strategic planning. Despite the potential benefits, the adoption of DDDM poses significant challenges, including data quality issues, integration complexities, and the need for a cultural shift towards valuing data analytics. Through a comprehensive analysis of recent research and case studies, this article synthesizes key findings, emerging trends, and future research areas in DDDM. It provides practical recommendations for practitioners aiming to implement and optimize DDDM processes, emphasizing the importance of fostering a data-driven culture, investing in robust data infrastructure, and ensuring the ethical use of data. Additionally, the article offers suggestions for continuous improvement and adaptation to technological advancements, advocating for regular strategy reviews, monitoring emerging trends, and fostering innovation. By addressing these challenges and leveraging the outlined recommendations, organizations can unlock the full potential of DDDM, driving significant advancements in efficiency, competitiveness, and strategic decision-making in the digital age.
 Keywords: Data-driven Decision Making, Engineering Management, Machine Learning, Big Data, Advanced Analytics, Organizational Efficiency, Data Quality and Infrastructure, Technological Advancements.