This paper is an outgrowth of a larger intrinsic case study on the development of an effective procedure to diagnose and identify appropriate strategies for supporting low-performing learners. The case involved a group of students preparing for the SPM examination who had lost hope of passing the History subject. The primary purpose of this paper is to emphasize the importance of data dissection and decontextualization of various types of learner data, including psychometric data, school-based assessment progress, and student profile and context data. This decontextualization is a crucial step for prescribing the correct interventions. The data were collected through conversational interviews, peripheral observations, and document analysis, using introspection and retrospection processes from participants who were directly or indirectly managing the case. The data dissection process followed the principles of Instructional Systems Design (ISD), which were applied using the ADDIE Model (Analysis, Design, Development, Implementation, and Evaluation) and the major outcome was the establishment of the Data Dissection-Diagnosis Framework. This framework may serve as a meaningful guideline for other schools where it demonstrates the effective use of people analytics in an educational setting to inform and facilitate early engagement with at-risk students. This innovation not only helps identify suitable learning strategies but also boosts student morale and motivation to strive for excellence. As each school is unique, the procedure can be adapted and refined to fit different contexts, cultures, and practices.