An Examination of the Roles of Business Analytics & Intelligence, Big Data, Predictive Analytics, Data Quality, and Data Analysis in Enhancing Decision-Making Effectiveness within the Retail Industry

This dataset has been created for the purpose of studying the impact of various components of data technology on the effectiveness of decision-making processes within the retail industry. The analysis consists of a structural model containing five independent components: Business Analytics and Intelligence, Big Data, Predictive Analytics, Data Quality, and Data Analysis, each of which is assessed against the dependent variable: Decision Making Effectiveness in the Retail Industry. Data collection is performed using a structured questionnaire which was administered to 455 students or people who have good knowledge about the research model that is used in this study. from Greater Jakarta during the period of May 2 to July 15, 2025. This study applies the technique of Structural Equation Modelling (SEM) to study the relationships between variables, making it possible to evaluate the contribution of each data technology factor on decision making effectiveness. The dataset is comprised of both the observed measurements as well as the latent constructs derived from the validated indicators, thus providing a sound statistical basis for testing the hypotheses and validating the model. Furthermore, the implementation of SEM provides in-depth analysis concerning the direct and indirect relationships of data technologies concerning the decisions in the retail sector. Important steps in the methodology are Model Specification, Path Analysis, Evaluation of Goodness of Fit, and Hypothesis Testing. The work provides an example of how advanced analytics are being applied in the retail sector, showcasing the pervasive role of data and analytics in facilitating evidence-based decision-making in an increasingly competitive landscape.