Analysis to Evaluate the Improvements and Obstacles of Data-Driven Decision-Making in Organisations

Abstract

This study explores the comparative effectiveness of traditional versus data-driven decision-making in management, focusing on the transition from intuition-based approaches to data-informed strategies. With digital transformation accelerating the availability and use of data, managers are increasingly tasked with integrating data analytics, AI, and ML into their decision processes. The study adopts a mixed-methods approach, incorporating a literature review, case study analysis, surveys of managers, and expert interviews to examine both decision-making approaches across various industries. Results reveal that DDDM offers substantial advantages over traditional methods in terms of accuracy, speed, and scalability, particularly in large organizations where decision-making complexity demands precision and adaptability. However, challenges such as data quality issues, high infrastructure costs, privacy concerns, and a notable gap in data literacy often hinder the successful implementation of DDDM. Findings from expert interviews highlight best practices for DDDM adoption, including investment in data quality, data literacy training, and ethical data usage guidelines to foster a data-driven culture within organizations. The study concludes that an optimal approach combines the strengths of both traditional and data-driven methods, leveraging data insights while retaining the context-driven judgment of experienced managers. This hybrid model enables organizations to balance scalability with nuanced decision-making, fostering sustainable growth in a dynamic business environment. Recommendations include strategic investments in data infrastructure, cross-functional collaboration, and an emphasis on ethical data practices. Future research could further examine industry-specific adaptations and the role of organizational culture in data adoption, as these factors significantly influence the success of DDDM initiatives. This research provides valuable insights for managers seeking to enhance decision quality and operational agility by integrating data-driven approaches into their strategic processes.