DRIVEN DECISION MAKING IN MODERN ORGANIZATIONS: A MANAGEMENT SCIENCE APPROACH

Abstract

The rapid growth of digital technologies, big data, and artificial intelligence has significantly transformed decision-making practices within modern organizations. Increasingly, organizations are adopting data-driven decision-making (DDDM) approaches to enhance operational efficiency, improve forecasting accuracy, and support strategic management processes. Within the field of management science, data-driven decision systems integrate data infrastructure, analytics capabilities, and artificial intelligence technologies to convert large volumes of organizational data into actionable insights. Despite the increasing adoption of analytics technologies, the extent to which data investment and analytics capability contribute to organizational performance remains an important research question. This study investigates the relationship between data investment, analytics maturity, artificial intelligence adoption, and organizational performance indicators. Using a quantitative analytical framework, the study examines a structured dataset containing variables related to organizational characteristics, analytics capability, and performance outcomes. Descriptive statistics, comparative analysis, and correlation assessment are employed to explore patterns within the dataset and evaluate potential relationships between analytics capability and performance indicators such as forecast accuracy, decision cycle efficiency, and KPI growth. The findings highlight that analytics capabilities represent an important component of modern organizational decision systems, but their effectiveness depends on organizational readiness and effective implementation.