Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/223286
Title: BIG DATA: THE FUTURE OF TOWNSHIP MANAGEMENT
Authors: GIAM HUI TING JASMINE
Keywords: Real Estate
School of Design and Environment
Yu Shi Ming
2019-2020 Real Estate
RE
Issue Date: 15-Nov-2019
Citation: GIAM HUI TING JASMINE (2019-11-15). BIG DATA: THE FUTURE OF TOWNSHIP MANAGEMENT. ScholarBank@NUS Repository.
Abstract: Town 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.
URI: https://scholarbank.nus.edu.sg/handle/10635/223286
Appears in Collections:Bachelor's Theses

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Giam Hui Ting Jasmine 2019-2020.pdf1.05 MBAdobe PDF

RESTRICTED

NoneLog In

Page view(s)

92
checked on Oct 6, 2022

Download(s)

41
checked on Oct 6, 2022

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.