Agile Analytics: Applying in the Development of Data Warehouse for Business Intelligence System in Higher Education

  • Reynaldo Joshua Salaki
  • Kalai Anand Ratnam
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 745)


Majority of the Higher Learning Institutions are being implemented with information management systems for all the core activities ranging from admission, registration, alumni, graduate and academy operations. The data generated by these integrated information systems are transactional in nature and has been increasing exponentially. However, the use and value of the data has not been fully explored for driving decision-making process. This paper explores the importance of Agile Analytics in Business Intelligence and Data Warehouse development among Higher Learning Institutions. The paper concludes by outlining future directions relating to the development and implementation of an institutional project on data analytics.


Agile analytics BI Data warehouse Framework 


  1. Aloush, S.H.A.: The role of data warehouse in decreasing the time of decision taking. Aust. J. Basic Appl. Sci. 9(5), 216–219 (2015)Google Scholar
  2. Ambara, M.P., Sudarma, M., Kumara, I.N.S.: Desain sistem semantic data warehouse dengan metode ontology dan rule based untuk mengolah data akademik universitas XYZ di Bali. Jurnal Teknologi Elektro 15(1), 1–8 (2016)Google Scholar
  3. Bossung, S., Stoeckle, H., Grundy, J., Amor, R., Hosking, J.: Automated data mapping specification via schema heuristics and user interaction. In: Proceedings of the 19th IEEE International Conference on Automated Software Engineering, University of Illinois at Urbana-Champaign, Illinois, USA, pp. 208–217 (2004).
  4. Moturi, C.A., Emurugat, A.: Prototyping an academic data warehouse: case for a Public University in Kenya. Brit. J. Appl. Sci. Technol. 8(6), 551–557 (2015)CrossRefGoogle Scholar
  5. Collier, K.: Agile Analytics: A Value Driven Approach to Business Intelligence and Data Warehousing. Agile Software Development Series. Pearson, Boston (2012)Google Scholar
  6. George, J., Member, I., Kumar, B.V., Kumar, V.S.: Data warehouse design considerations for healthcare business intelligence system. In: Proceedings of the World Congress on Engineering (WCE) London, UK, pp. 1–4 (2015) Google Scholar
  7. Horakova, M., Skalska, H.: Business intelligence and implementation in a small enterprise. J. Syst. Integr. 4(2), 50–61 (2013)Google Scholar
  8. Ellicium. 5 Data Warehouse implementation mistakes to avoid in Big Data Projects (2017).
  9. Irsyadi, A., Fatah, Y.: Implementasi Data Warehouse dan Data Mining untuk Penentuan Rencana Strategis Penjualan Batik. Jurnal KomuniTi Fakultas Komunikasi dan Informatika, Universitas Muhammadiyah Surakarta. 6(1), 42–58 (2014)Google Scholar
  10. Kumar, S.C., Kumar, S., Mohit, T.V., Mahesh, A.: Data mining techniques for banking applications. Int. J. Res. Stud. Comput. Sci. Eng. (IJRSCSE) 2(4), 15–20 (2015)Google Scholar
  11. Mitchel, T.: Why Data Warehouse Projects Fail (2017).
  12. Nejres, S.M.: Analysis of data warehousing and data mining in education domain. Int. J. Adv. Comput. Sci. Technol. 4(4), 35–38 (2015)Google Scholar
  13. Nisal, S.: SAS programming tips and techniques for data mapping. In: 20th Annual SouthEast SAS Users Group (SESUG) Conference, Seattle, Washington (2012).
  14. Prasetyo, A., Soedijono, W.B., Amborowati, A.: Perancangan Data Warehouse untuk Mendukung Perencanaan Pemasaran Perguruan Tinggi. Jurnal Telematika 10(1), 1–22 (2017)Google Scholar
  15. Pusadan, M.Y.: Rancang Bangun Data Warehouse. Graha Ilmu, Yogyakarta (2013)Google Scholar
  16. Rainardi, V.: Building a Data Warehouse with Examples in SQL Server. Apress, New York (2008).
  17. Rizzi, S., Abelló, A., Lechtenbörger, J., Trujillo, J.: Research in data warehouse modeling and design: dead or alive? In: Proceedings of the 9th ACM International Workshop on Data Warehousing and OLAP (DOLAP 2006), pp. 3–10 (2006)Google Scholar
  18. Salinas, S.O., Lemus, A.C.N.: Data warehouse and big data integration. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 9(2), 1–17 (2017)Google Scholar
  19. Sebaa, A., Chikh, F., Nouicer, A., Abdelkamel, T.A.: Research in big data warehousing using hadoop. J. Inf. Syst. Eng. Manage. 2(2), 1–5 (2017)Google Scholar
  20. Shivtare, S., Shela, P.: Data warehouse with data integration: problems and solution. IOSR J. Comput. Eng. (IOSR-JCE) 1(1), 66–71 (2015)Google Scholar
  21. TechTarget: Data warehouse development: four strategic steps (2017).
  22. Wijaya, G.: Perancangan Data Warehouse Nilai Mahasiswa Dengan Kimball Nine-Step Methodology. Jurnal Informatika 4(1), 1–11 (2017)MathSciNetGoogle Scholar
  23. Gonçalves, M.J.A., Rocha, Á., Cota, M.P.: Information management model for competencies and learning outcomes in an educational context. Inf. Syst. Front. 18(6), 1051–1061 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Computing, Engineering and Technology, School of Computing and TechnologyAsia Pacific University of Technology and InnovationKuala LumpurMalaysia

Personalised recommendations