From Big Data to Smart Decisions: The Influence of Augmented Analytics on Managerial Decision-Making Quality

Abstract

The exponential growth of big data has increased the complexity of managerial decision-making, challenging managers to transform vast and heterogeneous data into timely and high-quality decisions. In response to these challenges, augmented analytics integrating artificial intelligence, machine learning, and advanced analytics has emerged as a promising approach to enhance decision-making processes. This study aims to examine the influence of augmented analytics on managerial decision-making quality. Using a quantitative approach, data were collected through a structured questionnaire from 100 managerial-level respondents who actively utilize analytical systems in organizational decision-making. The data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) to evaluate both the measurement and structural models. The results demonstrate that augmented analytics has a positive and statistically significant effect on managerial decision-making quality, indicating its role in improving decision accuracy, speed, consistency, and relevance. The structural model shows a moderate explanatory power, suggesting that augmented analytics is a key determinant of decision quality while acknowledging the presence of other influencing factors. These findings contribute to the literature on information systems and decision-making by providing empirical evidence on the strategic value of augmented analytics beyond traditional analytics approaches. Practically, the study highlights the importance for organizations to move beyond data availability toward the intelligent integration of augmented analytics to support high-quality managerial decisions in data-intensive environments.