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International Journal of Academic Research in Business and Social Sciences

Open Access Journal

ISSN: 2222-6990

Identification of Business Intelligence in Managing Maintenance Management for Government Office Buildings in Putrajaya

Ain Farhana Jamaludin, Muhammad Najib Razali

http://dx.doi.org/10.6007/IJARBSS/v11-i6/10216

Open access

A proper data management in maintenance practices are required to make the daily process smooth. With the ability of Information Technology (IT) will influence on maintenance management database system. However, this IT systems are challenged with massive increases in amount of data, the speed they are generated and the need to record, process and visualize those data in real time to the user. This rapid growth of information results in the pervasion of Big Data (BD) and Business Intelligence (BI). This research highlights Public Works Department or Jabatan Kerja Raya (JKR) as a key pillar in managing and store important data relating to maintenance management for each building in Putrajaya. JKR finds difficulties to create sustainable maintenance policy which require the right tools and equipment to achieve sustainable goals and objective. Last research attentions have been given to the use of big data and business intelligence in maintenance management industries particularly in government sector. Hence, this research presents an analysis of the complexities and requirement for maintenance that focuses on intelligent system that help to improve the intelligent management of maintenance in making informed decision and can be applied horizontally to address identified challenges in practices.

Ajah, I. A., & Nweke, H. F. (2019). Big data and business analytics: Trends, platforms, success factors and applications. Big Data and Cognitive Computing, 3(2), 1–30. https://doi.org/10.3390/bdcc3020032
Aljumaili, M., Wandt, K., Karim, R., & Tretten, P. (2015). EMaintenance ontologies for data quality support. Journal of Quality in Maintenance Engineering, 21(3), 358–374. https://doi.org/10.1108/JQME-09-2014-0048
Athanasopoulos, G., Hyndman, R. J., Song, H., & Wu, D. C. (2011). The tourism forecasting competition. International Journal of Forecasting, 27(3), 822–844. https://doi.org/10.1016/j.ijforecast.2010.04.009
Dayal, U., Kuno, H., Wiener, J. L., Wilkinson, K., Ganapathi, A., & Krompass, S. (2009). Managing operational business intelligence workloads. Operating Systems Review (ACM), 43(1), 92–98. https://doi.org/10.1145/1496909.1496927
Desouza, K. C., & Jacob, B. (2017). Big Data in the Public Sector: Lessons for Practitioners and Scholars. Administration and Society, 49(7), 1043–1064.
https://doi.org/10.1177/0095399714555751
Dutta, P. (2019). Business Analytics using Microsoft Power BI and AWS Redshift. International Journal of Trend in Scientific Research and Development, Volume-3(Issue-2), 984–986. https://doi.org/10.31142/ijtsrd21545
Haneem, F., Kama, N., Taskin, N., Pauleen, D., & Abu Bakar, N. A. (2019). Determinants of master data management adoption by local government organizations: An empirical study. International Journal of Information Management, 45(April 2018), 25–43. https://doi.org/10.1016/j.ijinfomgt.2018.10.007
He, Y., Yu, F. R., Zhao, N., Yin, H., Yao, H., & Qiu, R. C. (2016). Big Data Analytics in Mobile Cellular Networks. IEEE Access, 4, 1985–1996.
https://doi.org/10.1109/ACCESS.2016.2540520
Walker, J. S. (2014). Big Data: A Revolution That Will Transform How We Live, Work, and Think. International Journal of Advertising, 33(1). https://doi.org/10.2501/ija-33-1-181-183
Jones, S., Ball, A., & Ekmekcioglu, Ç. (2008). The Data Audit Framework: A First Step in the Data Management Challenge. International Journal of Digital Curation, 3(2), 112–120. https://doi.org/10.2218/ijdc.v3i2.62
Kumar, A., Boehm, M., Yang, J., & Columbus, B. (2017). Data Management in Machine Learning: Challenges, Techniques, and Systems Who We Are Motivation: A Data-Centric View of ML. https://adalabucsd.github.io/papers/Slides_2017_Tutorial_SIGMOD.pdf
Lieberman, M. (2014). Visualizing Big Data?: Social Network Analysis By Michael Lieberman. Digital Research Conference.
Wang, J., Tang, Y., Nguyen, M., & Altintas, I. (2015). A Scalable Data Science Workflow Approach for Big Data Bayesian Network Learning. Proceedings - 2014 International Symposium on Big Data Computing, BDC 2014, 16–25.
https://doi.org/10.1109/BDC.2014.10
Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2014). Data Mining with Big Data Xindong. Ieeexplore.Ieee.Org, 1–26. https://ieeexplore.ieee.org/abstract/document/6547630/

In-Text Citation: (Jamaludin & Razali, 2021)
To Cite this Article: Jamaludin, A. F., & Razali, M. N. (2021). Identification of Business Intelligence in Managing Maintenance Management for Government Office Buildings in Putrajaya. International Journal of Academic Research in Business and Social Sciences, 11(6), 856–864.