THE ROLE OF BUSINESS INTELLIGENT SYSTEMS IN MONITORING AND ANALYSIS OF UNIVERSITY DATA
Keywords:business intelligence systems, university data analysis, support management decisions
Nowadays, it has become clear that the success of different sized organizations depends on the speed at which they adapt to the dynamic changes and challenges of competitive market structures. At the same time, it is considered that information is a key element for identifying the strengths and weaknesses of the business activities of organizations and the trends for their future development and market consolidation.
Modern universities face major challenges related to the processing of large amounts of data, which are continuously generated by different systems and units, but in most cases, the information flow is not analysed effectively enough. Namely the efficient extraction of educational data is an important aspect for the analysis of the state of the university as well as the effective planning of its future development.
Therefore, the main purpose of this study is to consider the capabilities of intelligent business analysis information systems to monitor and control the large volumes of data generated at the University of Forestry, Bulgaria. Implementing such a system will help transform data into valuable information and knowledge that will assist academic leadership in taking timely, informed, reasoned managerial decisions and actions, taking into account the dynamic and competitive educational environment and rapidly changing educational needs in higher education.
Clark, T. D., Jones, M. C., Armstrong, C. P. (2007). The Dynamic Structure of Management Support Systems: Theory development, research focus and directions. MIS Quarterly.
Dell’Aquila, C., Tria, F. Di, Lefons, E., Tangorra, F. (2008). Business intelligence systems: a comparative analysis, WSEAS Transactions on Information Science & Applications, Vol. 5, pp. 612-621.
Elbashir, M.Z., Collier, P.A. & Davern, M.J. (2008). Measuring the effects of business intelligence systems: the relationship between business process and organizational performance. International Journal of Accounting Information Systems, 9(3), 135153.
Howson, C., Richardson, J., Sallam, R. &Kronz, A. (2019). Magic quadrant for analytics and business intelligence platforms, Gartner, Retrieved from https://www.gartner.com/doc/3900992/magic-quadrant-analytics-business-intelligence.
IDC - International Data Corporation (2019). Forecasts Revenues for Big Data and Business Analytics Solutions Will Reach $189.1 Billion This Year with Double-Digit Annual Growth Through 2022. Available at: https://www.idc.com/getdoc.jsp?containerId=prUS44998419.
Isik, O. (2009). Business intelligence success: An empirical evaluation of the role of BI capabilities and organization's decision environment. AMCIS 2009 Doctoral Consortium, 1-13.
Mladenova, M., Zhelyazova, B. (2016). Application of e-learning platform Blackboard Learn in the University of Forestry, Sofia, Bulgaria. Innovate & Educate, Teaching & Learning Conference by Blackboard, April, Groningen.
Mladenova, M., Kirkova, D., (2019). Use of Alumni Network by the University of Forestry as a Mechanism for Monitoring and Evaluation of Group Indicators from the Higher Education System, Intel Entrance. ISBN: 978-954-2910-92-3 (in Bulgarian).
Monfared, J., Akbari, Z. (2019). Using Business Intelligence Capabilities to Improve the Quality of Decision-Making: A Case Study of Mellat Bank, World Academy of Science, Engineering and Technology, International Journal of Economics and Management Engineering, Vol:13, No:2, 2019, ISNI:0000000091950263.
Muntean, M., Sabau, G., Bologa, A. R., Surcel T. and A. Florea (2010). Performance Dashboards for Universities, 2nd International Conference on Manufacturing Engineering, Quality and Production Systems.
Neykova, M., Zhelyazova, B. (2017). Contemporary Training Methods in the Field of Business Intelligence Decision Support Systems. 2nd Conference on Innovative Teaching Methods (ITM 2017), Bulgaria, pp. 134. ISBN 978-954-21-0930-3.
Ramakrishnan, T., Jones, M. C., & Sidorova, A. (2012). Factors influencing business intelligence (BI) data collection strategies: An empirical investigation. Decision Support Systems, 52(2), 486-496.
Richardson, J., Sallam, R., Schlegel, K., Kronz, A., Sun J. (2020). Magic Quadrant for Analytics and Business Intelligence Platforms, Published 11 February 2020, ID G00386610. Retrieved from https://www.gartner.com/en/documents/3980852/magic-quadrant-for-analytics-and-business-intelligence-p.
Sallam, R., Howson, C., Idoine, C. (2017). Magic Quadrant for Business Intelligence and Analytics Platforms. Gartner, Inc.
Shariat, M., & Hightower Jr, R. (2007). Conceptualizing business intelligence architecture. Marketing Management Journal, 17(2), 40-46.
UL-Ain, N., Giovanni, V. & DeLone, W. (2019). Business intelligence system adoption, utilization and success – A systematic literature review, Proceedings of the 52nd Hawaii International Conference on System Sciences, January 8 – January 11, 2019, Grand Wailea, Maui.
Yeoh, W., & Koronios, A. (2010). Critical success factors for business intelligence systems. Journal of computer information systems, 50(3), 23-32.
Wen-Chen, Hu, Kaabouch, N. (2014). Big Data Management. Technologies and Applications, IGI Global, USA.
Watson, H. J., & Wixom, B. H. (2007). The current state of business intelligence. Computer, 40(9), 96-99.
Link to the university’s financial statements available on the website: https://ltu.bg/bg/%D1%83%D0%BD%D0%B8%D0%B2%D0%B5%D1%80%D1%81%D0%B8%D1%82%D0%B5%D1%82%D1%8A%D1%82/%D1%84%D0%B8%D0%BD%D0%B0%D0%BD%D1%81%D0%BE%D0%B2%D0%B0-%D0%B8%D0%BD%D1%84%D0%BE%D1%80%D0%BC%D0%B0%D1%86%D0%B8%D1%8F/%D0%B3%D0%BE%D0%B4%D0%B8%D1%88%D0%BD%D0%B8-%D1%84%D0%B8%D0%BD%D0%B0%D0%BD%D1%81%D0%BE%D0%B2%D0%B8-%D0%BE%D1%82%D1%87%D0%B5%D1%82%D0%B8
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