Big data use at an automotive manufacturer: a framework to address privacy concerns in Hadoop Technology.
| dc.contributor.advisor | Piderit, R | en |
| dc.contributor.author | Padayachee, Prenisha | en |
| dc.date.accessioned | 2026-08-20T07:23:11Z | |
| dc.date.available | 2026-08-20T07:23:11Z | |
| dc.date.issued | 2021-11 | en |
| dc.description.abstract | An automotive manufacturer can generate big data through accessible data points from internal and external Internet of Things (IoT) data sources connected to the production line. Big data analytics needs to be applied to these large and complex datasets to realise the associated opportunities, such as an improved manufacturing process, optimised supply chain management, competitive advantage and business growth. In order to store, manage and process the data, automotive manufacturers are using Apache Hadoop technology. Apache Hadoop is a cost-effective, scalable, and fault-tolerant technology. However, there has been a concern raised regarding the privacy of big data in Apache Hadoop. A key challenge in Hadoop technology is its ineffective security model, making the data susceptible to unauthorised users. Consequently, a breach in data privacy results in automotive manufacturers becoming victims of theft of trade secrets and intellectual property via corporate spies. This theft has a negative impact and results in the loss of company reputation, business competitiveness and business growth in the automotive market. This study investigated a solution to ensure big data privacy when using Hadoop technology. The Selective Organisational Information Privacy and Security Violations Model (SOIPSVM) and the Capability Maturity Model (CMM) provided the theoretical base for this study. The researcher undertook a literature analysis and qualitative study to understand and address the identified research problem. The primary data was collected from ten Information Technology (IT) specialists at a local automotive manufacturer. These specialists participated in an interview session, which also included the completion of a questionnaire. All questions were pre-determined and open-ended, and the participants' responses were recorded. Primary data was analysed using the inductive approach by identifying relevant themes and sub-themes. In contrast, the literature analysis included academic journals, conference proceedings, websites, and books, which were critically discussed in this study. This study's findings indicated various measures to be implemented by the automotive manufacturer to address the research problem. Critical success factors were derived from the identified measures, which addressed significant data privacy issues in using Hadoop technology. The identified critical success factors included: control of internal and external data sources; monitor the value of big data towards improving the automotive manufacturing process and user behaviour; implementation of user authentication; encryption to secure data; disaster recovery and backup plan; execution of authorisation and Access Control List (ACLS); conduct audits and regular reviews of user access to data; apply data masking to sensitive data and tokenization to secure data; build own infrastructure to store and analyse data; install regular security updates and update passwords regularly. Each factor had a purpose that examined big data management, governance and compliance in detail. The identified factors contributed towards ensuring data privacy in the use of Hadoop technology. These factors were categorised into contextual and rule and regulatory conditions adopted from the SOIPSVM. Identified conditions were then aligned to the five-level CMM. Each condition was expanded upon at various maturity levels to form a framework that addressed the main research problem. The framework's application was described as an independent assessment of each critical success factor and provided a guide through various maturity levels. The framework's purpose was to address and overcome big data privacy concerns in using Hadoop technology at a local automotive manufacturer.,Thesis (MCom) (Information Systems) -- University of Fort Hare, 2021 | en |
| dc.format | computer | en |
| dc.format | online resource | en |
| dc.format | application/pdf | en |
| dc.format | 1 online resource (211 pages) | en |
| dc.format | en | |
| dc.identifier.citation | Padayachee, Prenisha. 2021-11 | en |
| dc.identifier.other | vital:52009 | en |
| dc.identifier.other | http://hdl.handle.net/10353/22255 | en |
| dc.identifier.uri | http://hdl.handle.net/20.500.11837/4992 | |
| dc.language.iso | English | en |
| dc.publisher | University of Fort Hare | en |
| dc.publisher | Faculty of Management and Commerce | en |
| dc.rights | University of Fort Hare | en |
| dc.rights | All Rights Reserved | en |
| dc.rights | Open Access | en |
| dc.subject | Data protection. | en |
| dc.subject | Privacy, Right of. | en |
| dc.subject | Computer networks--Security measures. | en |
| dc.subject | Grahamstown (South Africa) | en |
| dc.subject | Eastern Cape (South Africa) | en |
| dc.subject | South Africa | en |
| dc.subject | History | en |
| dc.title | Big data use at an automotive manufacturer: a framework to address privacy concerns in Hadoop Technology. | en |
| dc.title.alternative | Big data use at an automotive manufacturer | en |
| dc.type | Master's theses | en |
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