This research paper investigates the application of the big data analytics lifecycle to measure student performance and evaluate the effectiveness of curriculum in educational institutions. Drawing on previous studies, the paper outlines a methodology encompassing all stages of the analytics lifecycle to analyze student data and identify factors influencing academic achievement. The discovery phase involves identifying key issues related to student performance and curriculum effectiveness, while the data preparation phase focuses on collecting and organizing relevant data for analysis. Through model planning and building stages, models are developed to capture relationships between variables such as attendance, participation, and digital learning resource utilization, and students’ final grades. The communication of results and operationalization phases emphasize the importance of disseminating findings to stakeholders and implementing targeted interventions based on research insights. Overall, this research highlights the potential of big data analytics to enhance educational outcomes by informing instructional strategies and fostering a conducive learning environment.