Educational Data Mining (EDM) offers significant potential to improve learning outcomes by applying advanced analytical techniques to educational datasets. The possibilities include building accurate predictive models to identify students at risk of failure, allowing for early, targeted interventions. EDM can also facilitate the discovery of patterns in student interactions with learning resources, revealing which instructional strategies are most effective. Furthermore, it enables the automatic discovery of relationships between course content and student performance, informing curriculum optimization. However, realizing these potentials faces substantial challenges, primarily involving ethical concerns surrounding student data privacy and security, as the volume of information collected is vast. A major technical hurdle is the heterogeneity and "messiness" of educational data, which is often poorly structured and resides in fragmented systems. Successfully implementing EDM requires institutions to develop sufficient staff expertise in both data science and pedagogical context to correctly interpret and act upon the analytical findings. Ultimately, the biggest challenge is translating sophisticated analytical insights into practical, effective, and equitable pedagogical action within the classroom.