Submitted in fulfilment of the requirements for the Degree of Master Technologiae: Public Management, Durban University of Technology, 2012. / The purpose of this study was to investigate data management as a strategic information tool and its importance at the Durban University of Technology. The problem revolved around, inter alia, data management and data accuracy as structured interventions impacting on sound decision making. There were many challenges identified with regard to data management at the Durban University of Technology. The research design adopted a quantitative methodological approach that was used for collecting data through the use of a precoded self administered questionnaire. The empirical component involved a survey method considering that in was an in-house investigation and the target population equated to only 174 respondents. A significant response rate of 74% was obtained using the personal method for the data collection. Several hypotheses were formulated relating to data quality initiatives, data owners and their responsibility and frequency of data analysis in order to determine accuracy. These were tested using the Pearson chi-square test as well as data that was analyzed to determine frequencies and percentages of responses. The data was analyzed using the computerized Statistical Program for Social Sciences (SPSS) program. There were some significant findings that emerged from the empirical analysis. A highly significant finding was that 95.31% of the respondents strongly agreed that data management and integrity is of utmost importance at the Durban University of Technology. One of the recommendation suggest that an imperative for the Durban University of Technology to manage its data as an asset, a policy on data integrity and integration policy should be developed and implemented. Another recommendation highlighted and staff should strive to attain proper classification on the database, considering that this directly impacts on the accuracy of the HEMIS submissions to the Ministry of Education for the state allocated subsidy. The study concludes with directions for further research as well.

M. Tech. Business Information Systems / Currently there is a high demand in the banking environment for real time delivery of consistent, quality data for operational information. South African banks have the fastest growing use and demand for quality data; however, the bank still experiences data management related challenges and issues. It is argued that the existing challenges may be leveraged by having a sound data architecture framework. To this point, this study sought to address the data problem by theoretically conceptualizing a data architecture framework that may subsequently be used as a guide to improve data management. The purpose of the study was to explore and describe how data management challenges could be improved through Data Architecture.

Data quality is one of many challenges experienced in electronic healthcare (e-health) services in South Africa. The collection of data with substandard data quality leads to inappropriate information for health and management purposes. Evidence of challenges with regard to data quality in e-health systems led to the purpose of this study, namely to prioritise data quality challenges experienced by data users of e-health systems in South Africa. The study followed a sequential QUAL-quan mixed method research design to realise the research purpose. After carrying out a literature review on the background of e-health and the current status of research on data quality challenges, a qualitative study was conducted to verify and extend the identified possible e-health data quality challenges. A quantitative study to prioritise data quality challenges experienced by data users of e-health systems followed. Data users of e-health systems in South Africa served as the unit of analysis in the study. The data collection process included interviews with four data quality experts to verify and extend the possible e-health data quality challenges identified from literature. This was followed by a survey targeting 100 data users of e-health systems in South Africa for which 82 responses were received. A prioritised list of e-health data quality challenges has been compiled from the research results. This list can assist data users of e-health systems in South Africa to improve the quality of data in those systems. The most important e-health data quality challenge is a lack of training for e-health systems data users. The prioritised list of e-health data quality challenges allowed for evidence-based recommendations which can assist health institutions in South Africa to ensure future data quality in e-health systems. / Computing / M. Sc. (Computing)

Data quality is one of many challenges experienced in electronic healthcare (e-health) services in South Africa. The collection of data with substandard data quality leads to inappropriate information for health and management purposes. Evidence of challenges with regard to data quality in e-health systems led to the purpose of this study, namely to prioritise data quality challenges experienced by data users of e-health systems in South Africa. The study followed a sequential QUAL-quan mixed method research design to realise the research purpose. After carrying out a literature review on the background of e-health and the current status of research on data quality challenges, a qualitative study was conducted to verify and extend the identified possible e-health data quality challenges. A quantitative study to prioritise data quality challenges experienced by data users of e-health systems followed. Data users of e-health systems in South Africa served as the unit of analysis in the study. The data collection process included interviews with four data quality experts to verify and extend the possible e-health data quality challenges identified from literature. This was followed by a survey targeting 100 data users of e-health systems in South Africa for which 82 responses were received. A prioritised list of e-health data quality challenges has been compiled from the research results. This list can assist data users of e-health systems in South Africa to improve the quality of data in those systems. The most important e-health data quality challenge is a lack of training for e-health systems data users. The prioritised list of e-health data quality challenges allowed for evidence-based recommendations which can assist health institutions in South Africa to ensure future data quality in e-health systems. / School of Computing / M. Sc. (Computing)

If you cannot measure, you cannot manage. This is an age old saying, but still very true, especially within the current South African cybercrime scene and the ever-growing Internet footprint. Due to the significant increase in cybercrime across the globe, information security specialists are starting to see the intrinsic value of logs that can ‘tell a story’. Logs do not only tell a story, but also provide a tool to measure a normally dark force within an organisation. The collection of current logs from installed systems, operating systems and devices is imperative in the event of a hacking attempt, data leak or even data theft, whether the attempt is successful or unsuccessful. No logs mean no evidence, and in many cases not even the opportunity to find the mistake or fault in the organisation’s defence systems. Historically, it remains difficult to choose what logs are required by your organization. A number of questions should be considered: should a centralised or decentralised approach for collecting these logs be followed or a combination of both? How many events will be collected, how much additional bandwidth will be required and will the log collection be near real time? How long must the logs be saved and what if any hashing and encryption (integrity of data) should be used? Lastly, what system must be used to correlate, analyse, and make alerts and reports available? This thesis will address these myriad questions, examining the current lack of log analysis, practical implementations in modern organisation, and also how a need for the latter can be fulfilled by means of a basic approach. South African organizations must use technology that is at hand in order to know what electronic data are sent in and out of their organizations network. Concentrating only on FreeBSD PF firewall logs, it is demonstrated within this thesis the excellent results are possible when logs are collected to obtain a visual display of what data is traversing the corporate network and which parts of this data are posing a threat to the corporate network. This threat is easily determined via a visual interpretation of statistical outliers. This thesis aims to show that in the field of corporate data protection, if you can measure, you can manage.