Leveraging Big Data Analytics for Competitiveness in South African Financial Institutions
Abstract and Figures
Due to hundreds of millions of financial transactions that take place
daily in the financial sector, Big Data has become a buzzword and a crucial
component in financial business operations. Consequently, handling this data is
becoming a major concern for many financial services. Much as this is so, the
nature in which this data is generated makes it complex to analyse using
traditional database management systems. Little empirical research has been
done to inform financial organisations on how to analyse Big Data to improve
business agility and competitiveness. More still, there is a lack of an appropriate
model that could be used by financial organisations to carry out Big Data
analytics. This paper sought to conceptualise a model for Big Data analytics to
improve organisational competitiveness by taking the case of South African
financial institutions. Collected data was analysed quantitatively and results
indicated that technological, organisational, and environmental perspectives
along with individual factors play a significant role in Big Data analytics
improving financial institutions’ competitiveness. The identified factors were
used to conceptualise a model that could be used to extend research in this
direction. Results of the study revealed that as new trends in computing increase,
traditional analytics currently used by many organisations gradually become
obsolete hence it is recommended that the developed model be validated with a
wide range of data from various organisations to identify the new factors that
might be salient.
![Three, four and five layered architectures of IoT (adapted from [14])](https://www.researchgate.net/profile/Phumzile-Mogoale/publication/369453463/figure/fig14/AS:11431281129522502@1679567238554/Three-four-and-five-layered-architectures-of-IoT-adapted-from-14_Q320.jpg)
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Assessing the information security awareness (ISA) of users is crucial for protecting systems and organizations from social engineering attacks. Current methods do not consider the context of use when assessing users’ ISA, and therefore they cannot accurately reflect users’ actual behavior, which often depends on that context. In this study, we propose a novel context-based, data-driven, approach for assessing the ISA of users. In this approach, different behavioral and contextual factors, such as spatio-temporal information and browsing habits, are used to assess users’ ISA. Since defining each context explicitly is impractical for a large context space, we utilize a deep neural network to represent users’ contexts implicitly from contextual factors. We evaluate our approach empirically using a real-world dataset of users’ activities collected from 120 smartphone users. The results show that the proposed method and context information improve ISA assessment accuracy significantly.
Objective
The use of personal devices for work purposes (BYOD) is in high demand among healthcare professionals as it improves productivity. However, it can also lead to an increased risk of leaking sensitive patient information, being a major challenge for hospital management. The authors identified a dearth of empirical studies concerning BYOD security practices in hospitals. Hence, the aim of the paper was to report BYOD security practices in Australian hospitals through a national survey, first of its kind.
Methods
Online survey was conducted among hospital IT personnel, enquiring about BYOD security practices. 28 responses were collected (21 hospital groups; 7 standalone hospitals), representing approximately 100 public hospitals. Descriptive statistical analysis and cross-tabulation were carried out on survey data.
Results
BYOD was generally allowed across most device types (smartphone:100%; tablet:70%; laptop:70%) and operating systems (apple:100%; android:96%; windows:74%), but generally for the use of basic services such as email (100%). Majority of hospitals use generic security technologies (firewall:87%, two-factor authentication:70%, VPN:65%), whereas BYOD specific technologies (MDM:43%, containerisation:39%, UEM:4%) haven't been widely adopted. Less than half of the surveyed hospitals have a BYOD policy (45%). Staff education though provided in majority of surveyed hospitals (60%), is not comprehensive and regular. Lack of engagement with clinical staff for BYOD strategy development was also found (23%).
Conclusion
This research provided a comprehensive view of socio-technical BYOD security practices in Australian hospitals, highlighting its strengths, weaknesses, and the consequential implications, which can therefore provide useful insights for hospital IT executives and health departments.
