Analysis and Visualization of Tracer Study Data Through Kimball Four-Step Method and Tableau

Abstract

Tracer studies serve as a pivotal survey mechanism to assess the efficacy of educational systems and the compatibility of graduates with labor market requirements. This research leverages Big Data technologies alongside Tableau to scrutinize tracer study data gathered from alumni of Politeknik Caltex Riau (PCR) over the period from 2018 to 2022. Employing the Four-Step Kimball methodology, the study regularly undertakes data collection, processing, validation, and storage within a MongoDB database, prior to generating visual representations through Tableau. The analytical framework incorporates descriptive statistics, correlation analysis, and regression models to examine critical variables, including the alignment between academic disciplines and occupational roles, as well as the spatial distribution of graduates across regions. The visualizations produced facilitate data-driven decision-making, enabling enhancements in curriculum design, the advancement of career support services for alumni, and the fortification of ties with industrial stakeholders. Key results reveal a significant positive relationship between graduates' Grade Point Average (GPA) and their income levels, alongside a consistent year-on-year rise in participation rates for tracer studies, with the rate reaching 99.06% by 2022. Furthermore, the findings underscore notable trends in employment sectors and geographic mobility, with 74.58% of alumni employed within Indonesia, predominantly in Riau Province. These outcomes affirm the robustness of the implemented data analysis framework in bolstering policy formulation for educational institutions. Beyond immediate implications, the study highlights the potential of integrating scalable data management systems with advanced visualization tools to address the evolving challenges of alumni tracking and institutional accountability in higher education.