Browsing by Author "Rautenbach, Simon"

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    A maturity model for data science in small and medium-sized enterprises from developing countries
    (Stellenbosch : Stellenbosch University, 2024-02) Rautenbach, Simon; De Kock, Imke; Grobler, Jacomine; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: Data science (DS) is an industry 4.0 (I4.0) technology with the potential to improve organisational decision-making through the use of insights previously unknown, which may provide a competitive advantage to an organisation. Small and medium-sized enterprises (SMEs) are known to be large contributors to economies across the globe, based on metrics such as the gross domestic product (GDP) and the total share of the workforce. SMEs are generally less inclined to implement I4.0 technologies due to various challenges, such as a lack of capital and skills, which is detrimental to their development and competitive advantage. It can be argued that the successful implementation of DS has the potential to improve organisational decision-making in SMEs and increase their competitive advantage. A semi-structured literature review was conducted to explore the different I4.0 technologies, along with SMEs and how they fit into economies across the globe. A structured literature review was then performed to contextualise the problem, and to understand the factors that influence the successful implementation of DS in SMEs — both from developing and developed countries. Several challenges and opportunities associated with the implementation of DS were identified. Furthermore, a lack of research products in literature that address these challenges was identified, especially within the context of developing countries. The aim of this research was thus to contribute towards the successful implementation of DS in SMEs from developing countries. A requirement specification chapter revealed that a maturity model would be the most suitable research product for this study. Maturity models are designed as tools that enable the user to measure the current state of maturity for various domains within an organisation. Consequently, this study investigated the use of a maturity model as an appropriate research product for the implementation of DS in SMEs from developing countries. In pursuit thereof, the data science maturity model for SMEs (DSMMSMEs) was developed. The model consists of five domain components, which may be viewed as high-level categories of the given domain, which is DS for the purpose of this study. The chosen domain components are data, infrastructure, people, management, and governance. The DSMMSMEs was developed based on a foundational knowledge of current literature, which consists of various maturity models and research pertaining to DS and SMEs from developing countries. The development of the model followed a rigorous methodology for maturity model development, which is widely accepted across literature. Therefore, this research may be described as a non-empirical, qualitative study, consisting of both inductive and deductive approaches in an investigative manner. Once the DSMMSMEs had been developed, it was subject to a verification process which consisted of subject matter expert interviews. The feedback from the interviews was used to develop a second iteration of the model, which addressed any concerns raised by the subject matter experts. Next, the model was subject to a validation and implementation stage, which evaluated the appropriateness in terms of applicability, practicability, and usability. The findings of the implementation and validation stage showed that the DSMMSMEs is an appropriate tool that may contribute towards the successful implementation of DS in SMEs from developing countries.