Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/223286
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dc.titleBIG DATA: THE FUTURE OF TOWNSHIP MANAGEMENT
dc.contributor.authorGIAM HUI TING JASMINE
dc.date.accessioned2019-11-22T03:34:54Z
dc.date.accessioned2022-04-22T20:29:18Z
dc.date.available2019-12-26
dc.date.available2022-04-22T20:29:18Z
dc.date.issued2019-11-15
dc.identifier.citationGIAM HUI TING JASMINE (2019-11-15). BIG DATA: THE FUTURE OF TOWNSHIP MANAGEMENT. ScholarBank@NUS Repository.
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/223286
dc.description.abstractTown Councils (TCs) adopt various systems to track datasets required to sustain an effective Township Management, but with the rise in technology, they are behind in comparison to other sectors in adopting technologies such as Big Data Analytics (BDA) which allows for faster analysis of great volumes, variety and velocity of data. Despite the opportunities BDA presents, the Township Management industry has been slow in adopting BDA. This study examines, the drivers, potential applications and challenges, and also proposes strategies to overcome the challenges during the employment of BDA. A literature review was carried out to study prior success of BDA in Facilities Management and to examine the functions and various technological applications in the Town Councils. Following extensive literature reviews, surveys and in-depth interviews were conducted with industry professionals to explore BDA as the future of Township Management. The study affirms that BDA has great importance to the industry but still in its pre-adoption stage. Out of all the responses received, this study has narrowed down on three key drivers behind embedding BDA in Township Management followed by four potential applications of BDA. Advancing through this study, two major challenges in the adaptation have also been raised. This study ends with four ideas to merge BDA into Township Management. Firstly, the Managing Agents (MAs) should improve data ownership either by sharing databases among different agencies or introducing new systems to collect their own data. Secondly, MAs should clearly define the business case for funding and roll out small-scale pilot projects to test feasibility. Thirdly, necessary infrastructure and data warehouse must be designed to collect and analyse Big Data. Finally, MAs should build in-house capabilities by upskilling the existing workforce or recruiting individuals with the relevant skillsets.
dc.language.isoen
dc.sourcehttps://lib.sde.nus.edu.sg/dspace/handle/sde/4646
dc.subjectReal Estate
dc.subjectSchool of Design and Environment
dc.subjectYu Shi Ming
dc.subject2019-2020 Real Estate
dc.subjectRE
dc.typeDissertation
dc.contributor.departmentREAL ESTATE
dc.contributor.supervisorYU SHI MING
dc.description.degreeBachelor's
dc.description.degreeconferredBACHELOR OF SCIENCE (REAL ESTATE)
dc.embargo.terms2019-12-26
Appears in Collections:Bachelor's Theses

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