Faculty of Commerce, Law and Management (ETDs)

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    Exploring the big data maturity level of a metropolitan municipality in Gauteng Province, South Africa
    (University of the Witwatersrand, Johannesburg, 2024) Mathane, Tlou Phillemon; Mayayise, Thembekile
    A recent study by the World Bank highlights the importance of public sector organizations to embark on digital transformation. Public sector organizations that successfully undertake digital transformation journey serve their customers better; become more competitive; and improve their financial performance. Significantly, they also improve their digital maturity levels. Using a case study design, this study explored the big data maturity status quo of the Tshwane metropolitan municipality. Data maturity assessments are needful in the public sector to assist them in digital technology adoption. Conceptually, the study used the Resource View (RBV) theory to understand the extent to which this city optimizes big data as a strategic resource for decision-making. The study also used the Dynamic Capabilities Theory (DCT) to explore the extent to which big data analytics is leveraged to enhance capabilities of the city to improve service delivery. In this regard, the study focussed on five themes, viz: (a) organizational vision and strategy, (b) customer relations management, (c) data-driven- decision-making, (d) data governance, and (e) deployment of industry 4.0 best practices and/or systems. Following a qualitative design, the researcher collected data from 20 managers, using two data collection strategies. First, a focus group discussion was used to collect data from 8 managers at operational management level. A purposive sampling method was used. Secondly, structured questionnaires were administered to 12 managers, of which 6 were middle management level, and 6 were top management level incumbents. The study finds that there is no common understanding regarding the vision and strategy for digital transformation in the city. Big data analytics is not optimally used for purposes of innovation, and operational and strategic decision-making. This study contributes by uncovering some of the challenges faced by public sector organisations as far as using data to drive decision-making is concerned. In this regard, the study also tables some of the remedies and interventions that can be embarked upon to undermine some of the key teething challenges
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    Bridging Big Data Analytics skills gap in the financial services sector
    (University of the Witwatersrand, Johannesburg, 2024) Johnson, Reece; Godspower-Akpomiemie, Euphemia
    As the significance of big data analytics continues to escalate within the banking industry, there is a parallel surge in demand for adept professionals in this domain. Notably, one of the foremost challenges in the era of data is the scarcity of individuals possessing the requisite skill set to transform raw data into actionable insights that generate business value. Addressing this pressing concern, this study sets out to ascertain the technical and business-oriented data analytics skills crucial in the banking sector, pinpointing the most pivotal skills required both presently and in the forthcoming years. This qualitative research used collected data from banking institutions in South Africa and an academic institution via semi-structured interviews. A purposive sampling method was applied to select fourteen participants including executives and senior managers who have decision-making authority. A thematic data analysis was used as a lens to determine the critical skills required to effectively execute Big data and analytics in the banking industry. The findings revealed that critical technical, business, and interpersonal skills are in short supply among graduates and professionals in the South African banking industry. The study concludes and recommends that institutions of higher learning must enhance their curricula. This includes the incorporation of simulations and experiential learning, which are deemed particularly paramount
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    The role of data analytics in formulating a business model in the South African metals manufacturing sector
    (University of the Witwatersrand, Johannesburg, 2020) Maimela, Kelebogile; Munkuli, Bongani
    Technological advancements are a contributing factor to the success of any business, especially with globalisation mandating flexibility within businesses. The survival of the metals manufacturing companies is dependent on many variables, but the focus will be placed on the role of data analytics in business models. A quantitative approach was used to collect the data utilising Qualtrics software and data were recorded on Excel before being coded and then loaded onto the Statistical Package for Social Sciences (SPSS) software system. All employees in the metals manufacturing companies in South Africa made up the population for this study. The results revealed a relationship between data analytics and business insight involved in developing a business model. In the absence of data, the level of success in decision making is compromised. Over 80 percent of respondents emphasised the importance of data required in making decisions. The ability to make informed decisions gives companies a competitive edge, but a dynamic capability is evidenced through people’s experience in data analysis. The data collected were analysed using quantitative data analysis tools such as chi-squared tests and Cramer’s phi tests, which indicated that data play a pivotal role in developing business models
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    Big data and return on marketing investment in a South African insurer
    (University of the Witwatersrand, Johannesburg, 2021) Grater, Darryl; Beder, Laurence
    The purpose of the study centres around the enablement of the information and knowledge harvested from big data into increasing the return on marketing investment (ROMI). Many financial services providers offer a wide variety of financial products which include life insurance, health insurance, investment products, wellness and short-term insurance – most of these markets are saturated with multiple product providers all competing for share of potential customers’ attention and ultimately share of wallet. Although there are various studies involving the use and interpretation of big data in terms of propensity models and product recommender systems, cross-sell propensity modelling using personalisation based on existing financial products enjoyed by a customer has not been sufficiently or adequately researched - particularly for a short-term insurer within a Southern African context Considering this was a big data study, the research was quantitative whereby 566,758 customer profiles from a South African financial services provider (FSP) were extracted from the FSP’s databases, and thereafter scrutinised. The data was analysed using statistical methods which included Extreme Gradient Boosting to investigate and assess the impact of various existing customer characteristics and the acceptance of a personal lines short-term insurance quote when offered. The study embarked to understand if certain characteristics of existing customers could be identified which indicate materiality to short-term insurance product acceptance, and how one by using this personalisation information can create ring-fenced segments of existing customers for focused insurance product cross-sell campaigns, with better sales results versus standard busines development. Results identified in this research conclude that the theoretical framework supports the results exposed in this study. Certain customer characteristics from their product utilisation from life insurance, health insurance, banking, wellness and investment products indicate traits which are highly material to their conversion rate and short-term insurance product acceptance. By using this personalisation information and creating segments which portray the best conversion rates, focused sales campaigns result in higher sales ratios and therefore marketers and business development executives see better sales conversion rates versus mass-market, broad-based initiatives (for example random blind, cold calling). Sales resources can therefore be deployed to quote short-term insurance products to these customer segments and will render higher sales and thus a higher return on marketing investment. In conclusion, the contribution of this research study enables the financial services industry to focus on cross-sell and upsell campaigns with higher sales performances by using the personalisation insights from their existing customer base. Furthermore, marketers and business development executives can use similar frameworks to develop similar propensity models for other financial products. The research also opens a path for future similar research to be conducted across various other financial products (not only on short-term insurance impact) and across geographies extending further than a South African customer base
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    The perceived value of data analytics in the South African banking sector
    (University of the Witwatersrand, Johannesburg, 2023) Khedama, Thembela; Munkuli, Bongani
    Big data analytics is a technological process that covers the processing, arranging, categorization, and analysing of large voluminous data for purposeful usage by organizations. Big data analytics has in recent times become a buzz word as it proliferates exponentially within the banking sector in South Africa due to the maturity of storage, information technology and human skills within the domain of data. Despite this, there is little literature on big data analytics within the South African banking industry. It is evident that there is utilization of big data analytics tools and processes in the banking sector, but one question remains, is there value in South African banks derive from integrating big data analytics. The objective of this study is to understand the perception on the value of big data analytics held by South African banking senior management and executives. The researcher aims to understand if big data analytics is a mere tool to perform basic analysis with little to no power to add value in business outcomes. The perception of seeing BDA as an analysis tool has led to the undervaluing of big data analytics within the South African banking industry as there are no group executive seats with data or analytics titles. To achieve the intended research outcome of this study this research adopts the qualitative approach method. The data is collected through unstructured interviews across four major banks in South Africa. A purposive sampling strategy was employed in selecting the respondents. Data analysis is conducted through thematic analysis. This study found three global themes banking executives perceived to be valuable from adopting big data analytics. These were: (1) The state of big data analytics in banking, (2) Data Skills, (3) Driver of insights. The sub themes from the state of big data analytics in banking were: (1) Processing of the right data, (2) High volumes of data in the repository, (3) advanced analytics and the right tools to analyse data. The sub themes from data skills were: (1) resources, (2) technical skills. Lastly, the sub themes from the driver of insights were: (1) organization’s performance, (2) competitiveness in banking, (3) fighting fraud and financial crime.
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    The Factors Influencing the Adoption of Big Data in the Financial Services Sector in South Africa
    (University of the Witwatersrand, Johannesburg, 2023) Toma, Upenyu; Ndlovu, Chiedza
    This investigation examines factors influencing the adoption of big data in the South African banking sector. The dearth of studies on big data in the sector inspired the research. The investigation interrogates factors that influence the adoption of big data using the TOE model. The study used a total of 173 respondents across 53 South African banks. The primary data analysis model used was linear regression. The study’s findings are as follows: [1] Government regulations, competitor’s actions, and customer demands significantly influence adoption of big data. [2] Technology has a significant influence on the adoption of big data. [3] Organizational factors have a significant influence on the adoption of big data. Regression analysis showed that the dependent variable, big data adoption, is either positively or negatively affected by the study's variables. The study conducted a hypothesis test, which showed enough evidence to accept all alternative hypotheses suggesting a relationship between the variables and big data adoption. The study concludes that factors in the TOE model influence the rate of big data adoption in the banking sector. The study recommends that the government reduce regulations hindering big data adoption. The industry is encouraged to invest in big data for sustainable competitive advantage.
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    Challenges of big data usage for risk management in a South African Bank
    (University of the Witwatersrand, Johannesburg, 2023) Mosiane , Boipelo; Ochara, Nixon
    Risk management is a critical component in the effective operation of corporations, particularly for the purpose of identifying, assessing, and mitigating potential risks associated with running a business. In recent years, the exponential growth of data, along with technological advancements, has opened new opportunities for organizations to enhance their risk management capabilities. Big data is said to be a game-changer that has the potential to completely alter how different industries conduct business. However, there are several difficulties in effectively employing big data in risk management processes. Using a qualitative research approach, this research report highlights the usage of big data in risk management, emphasizing the potential benefits, challenges, and critical considerations for successful adoption. The research findings revealed that big data can enhance risk identification accuracy, proactive risk mitigation, strategic decision-making, and overall organizational resilience. The challenges hindering the adoption of big data in risk management are addressed, including skill gaps, data quality, technology infrastructure, talent acquisition, and bureaucratic barriers. The study highlights issues preventing widespread integration of big data in the risk community, particularly data trust and collaboration barriers between risk and technology teams. The research report recommends that the bank creates a robust talent acquisition strategy for analytics experts and prioritize retaining them to safeguard data resources. It also suggests fostering a learning-friendly environment for big data topics through accessible certifications and learning programs. Additionally, the research report emphasizes the need for addressing data quality issues in risk management, proposing solutions like RPA to improve data capture processes and enhance data accuracy and trust.
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    Sustaining and supporting SMME use of big data in South Africa
    (2021) Xegwana, Siviwe
    SMEs play a significant role in the South African economy because of their contribution towards employment which makes this a very important sector. Big Data plays a significant role in propelling the digital economy and organisations that are dominant across various industries are those that have effectively adopted and assimilated BDA technologies in their business strategies. The purpose of the study was to investigate the influence of Big Data Analytics BDA) technologies on sustaining growth strategies of Small and Medium-sized Enterprises in South Africa. The study was carried out using a non-experimental quantitative research method that draws from a post-positivist worldview. Data collection was done using an online survey in a form of a self-administered questionnaire. The research survey was distributed through email to a random sample of participants sourced from online business directories and IT professionals who work for SME entities and are actively involved in big data initiatives. The key findings of the study reveal that BDA technologies do positively influence SME’s competitive advantage, and resource constraints do have a negative impact on SME growth strategies, but the relationship between adoption and assimilation of BDA technologies and SME growth was found to be very weak and insignificant. Leveraging and delivering value through BDA, SMEs can develop and sustain a robust SME sector and make a considerable contribution towards employment and improve socio-economic conditions for the South African public.
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    Monetising big data in South African financial services
    (University of the Witwatersrand, Johannesburg, 2022) Ramruthan, Desmond
    Big data has grown to be an influential element in society impacting both individuals, governments and businesses. As its impact has accelerated on the corporate world, it was prudent to comprehend the effect that big data will have on these organisations. The purpose of the study was deliberate in its attempt to establish an understanding of the factors which influence an organisations ambition to monetise its big data assets. The study focused specifically on South African based entities operating within the financial services industry. With an increasingly digital financial market, big data contributes to improving revenue, increasing market share along with improving customer experience resulting in creating additional business value. The qualitative research process used semistructured interviews, established a set of principle findings, detailing seven key factors which can influence an organisations data monetisation plan. These findings were categorised into factors that are internal to the organisation suggesting that with appropriate tactics executive leadership can control or influence these factors. Some factors are external to the organisation to which senior management has limited control but can respond to these factors through a coordinated strategic approach. The research provides a suggestion to senior executives with an overview of these factors and recommendations localised to the South African financial markets on how to respond to each of the factors.
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    Non-asset digital platforms for the personal care industry in South Africa
    (2020) Van den Berg, Anton
    This research project was conducted to examine if the South African personal care industry will adopt a non-asset based digital platform business model on creating more economic opportunities for graduate students within the personal care industry in South Africa. The research was done through two sets of data collection through quantitative survey questionnaires. The one set at understanding the supply-side characteristics necessary for a successful non-asset based digital platform within the personal care industry in South Africa and if the proposed platform business model could be successfully adopted by the supply-side, meaning personal care service providers who deliver services such as beauty, nail treatments, make-up treatments etc. The second quantitative questionnaire was to understand the characteristics the consumers of personal care services within South Africa would value to adopt the proposed new business model. Both sets of research revealed that the market is favourable for the adoption of the proposed new business model and will adopt it if it creates more economic benefit for both sides of the platform as well as convenience. To conclude, the research and frameworks used throughout this research project have highlighted critical characteristics from a platform design as well as the necessary facilitating conditions and characteristics needed for a successful implementation of the proposed transformative business model.