Analytical Technology Infrastructure and Decision Making Efficiency in The Retail Sector

This dataset was designed to investigate the impact of data-driven technology components on decision-making effectiveness in the retail industry. The structural model examines three independent constructs Business Analytics and Intelligence, Predictive Analytics, and Data Quality and their influence on Decision-Making Effectiveness. Data were collected through a structured questionnaire distributed to 455 respondents from the Greater Jakarta area between May 2 and July 15, 2025. Respondents consisted of students and individuals with sufficient knowledge of the research framework. Structural Equation Modelling (SEM) was employed to analyze the relationships among variables and to assess the contribution of each construct to effective decision-making. The dataset comprises observed indicators and validated latent constructs, providing a robust basis for hypothesis testing and model validation. The SEM approach enables the evaluation of both direct and indirect effects among variables through model specification, path analysis, goodness-of-fit assessment, and hypothesis testing. This study highlights the importance of data-driven technologies in supporting evidence-based decision-making within the competitive retail environment.