One of the key challenges of fulfilling compliance requirements of international accreditation bodies such as the Association to Advance Collegiate Schools of Business (AACSB) is providing evidence of granular competency-level program learning outcomes. This endeavour demands manipulating and managing structured and unstructured datasets from multiple sources including unit outlines, rubrics, analytics (Learning Management System), assessment tasks, taxonomy levels, learning stage, analytics data from LMS, and course/campus/student-level data, among others for generating multiple analytics and visualisation pieces of evidence promptly to facilitate closing the loop intervention initiatives. To address this problem, we adapted the established software development approach of the iterative waterfall model, exploiting the flexibility in correcting process errors in an agile fashion. Additionally, we utilized a range of tools and techniques including machine learning technologies, significantly improving the efficiency and effectiveness of generating Assurance of Learning evidence fulfilling the compliance requirements of accrediting bodies and taking timely action to close the loop. The implications of this research will help the effective management of complex datasets and a systematic approach for generating evidence results in similar complex projects.