Evolution in managing information systems and the big datasets that increasing every minute, generates the vital demand competition to explore the maximum benefits of the "big data" creating in higher education institutes . Business intelligence (BI) and learning analytics (LA) are nowadays draw attention in business and society and being one of the most competitive advantages themes and profitable areas of interest in industry and organizations, including higher education institutions. Business intelligence Predictive analytics systems, using BI tools have established substantial effect on tactical transformation, decision support and informing trends" predicting. Learning analytics is working to deploy the worth of business intelligence in the academic context arena of education and training as it influences students" success, retention and explore enhancement to improve satisfaction. These concepts extent the process from envisioning the problem to applying learning analytics techniques to a particular situation, achieving insight to help deploy the results to improve decision-making.

This paper is based on a real dataset in a case study grounded in "Computer Science and Information Technology" college in Sudan university of science and technology (SUST), and focuses on investigating the potential of applying Business Intelligence approaches toward an expressive analysis of the organization"s and student"s experience, by making use of "business intelligence enhanced learning analytics framework", showing challenges and opportunities. It provides valuable insights into build a profound knowledge about students" experience, so as to assess the situation in teaching- learning process, by identifying weaknesses to be considered through practical institutional responses and, prepares for smart informative supervision.

The findings explored that, how technology captured data of students" performance for prediction, to identify at risk students, for the purpose of consulting and withholding them before being drop out, investigate the states and provinces that have very small number of students, the effect of number of students per college/department on average GPA, the individual student details are showed, such as his personal profile and his results in his complete educational life and the completion rate for graduated students is calculated. Moreover, how that constructions can take the form of statistical outlines, models, KPIs, insightful and interactive dashboards and relationships, offering a grand challenge for technology enhance learning (TEL).