Abstract
The university's main objectives go around excellence in teaching, research activities and both university and community services. These pillars are supported by a number of important resources and functional systems such as complex student's life-cycle management system that must be considered in any strategy development process. This paper discusses the role of the big data in the smart university campus architecture and strategies development process, and how it could be utilized in various stages of strategy development. Additionally, it highlights the university's ICT architecture, the foundation of the knowledge management system that has been implemented, to help the university harness knowledge out of the big data that is generated by various parties and scattered systems around the university. Furthermore, the chapter highlights some of the successes on how, with help of a BI tool, the university was able to make critical and quick decisions by tapping into the big data of more than 35 years of student records; one of the pillars of the smart university strategy.
TopIntroduction
In the last 10 years, big data and data analytics has evolved from a concept to a multibillion business. Recently the universities started following the trend to reap the benefit as well. Among the many uses, some institutions have employed data analytics to create more personalized approaches to advising. Others have leveraged predictive analytics to estimate the likelihood of student progress through courses and majors. Still others have used such analytics to combine data with teaching and advising to help improve student outcomes, particularly among underserved students (Gagliardi & Wilkinson, 2017).
From the bigger perspective, when developing a strategy and architecture for a Smart University, big data plays a major role in providing insight for various strategy domains such as predictive analytics, virtual campus, IoT and management dashboards, as described in the coming section.
Over their academic service lifetime, faculty members produce huge amount of information and knowledge as a result of various activities such as teaching, research activities, university and community services. Unfortunately, and for cultural, technical and management reasons, this information is never captured and maintained, and hence the opportunity of creating value and knowledge is lost. Furthermore, lots of effort are wasted in gathering and aggregating such information every time the management to either measure the progress and achievements of the university, or even when faculty members would like to apply for their promotion need it (Anirban, 2014; Ling, 2011). To be more specific, the current challenges are summarized as follows:
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Loss of faculty member’s activities tracking over their service lifetime.
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Huge efforts are required, to gather the needed info, every time the university annual report is required to develop.
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The strategy stays on paper and never cascaded down properly to the execution level, hence, no feedback is received on what and how much of the strategy has been implemented and objectives met.
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The previous point leads to disconnection between the developed university objectives and the annual objectives in the personal development plans (PDP) assigned to the faculty and other members.
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Faculty members are never aware of the complexity of the promotion cycle and information required until the time of application. Most of the time it is based on assumptions that they are ready. However, when the time comes, they hit a hard wall and face a major challenge in putting together a huge amount of promotion bylaw requirements and supporting evidence.
Key Terms in this Chapter
Business Intelligence Software: Is a type of application software designed to retrieve, analyze, transform, and report data for business intelligence. The applications generally read data that has been previously stored, often—though not necessarily—in a data warehouse or data mart.
ICT Application Evolution: IT applications systems and services evolution over certain number of years (that could extend over 10 years) showing how it changes according the industry trends.
Performance Eco System: A set of rules and processes to measure the performance of any set of activities for an entity and its employees.
Strategy Pillars: Set of high-level objectives that defines the areas of activities and initiatives that directs the organization achieve its long-term goals. Usually it is the output of the strategy exercise conducted in the organization.
Big Data: Is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.
Faculty Knowledgebase: A repository of the university faculty academic activity and information over lifetime of his/her service, such as research, services, publications, teaching, etc.
Campus Architecture: Is a conceptual high-level drawing of information and communication technology (ICT) components that makes up the any university layers of systems that serves different levels of employees in such organization.