A maturity model for data science in small and medium-sized enterprises from developing countries

Simple item page
dc.contributor.advisorDe Kock, Imkeen_ZA
dc.contributor.advisorGrobler, Jacomineen_ZA
dc.contributor.authorRautenbach, Simonen_ZA
dc.contributor.otherStellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.en_ZA
dc.date.accessioned2024-02-13T18:19:44Zen_ZA
dc.date.accessioned2024-04-27T02:29:59Zen_ZA
dc.date.available2024-02-13T18:19:44Zen_ZA
dc.date.available2024-04-27T02:29:59Zen_ZA
dc.date.issued2024-02en_ZA
dc.descriptionThesis (MEng)--Stellenbosch University, 2024.en_ZA
dc.description.abstractENGLISH 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.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Data wetenskap (DW) is ’n industrie 4.0 (I4.0) tegnologie met die potensiaal om besluitneming te verbeter met die gebruik van voorheen onbekende insigte, wat ’n mededingende voordeel aan ’n organisasie kan bied. Klein en medium-grootte ondernemings (KMOs) is groot bydraers tot ekonomie¨e regoor die wˆereld, gebaseer op maatstawwe soos die bruto binnelandse produk (BBP) en die totale bydra tot die arbeidsmag. KMOs implementeer selde I4.0-tegnologie¨e as gevolg van verskeie uitdagings, soos ’n gebrek aan kapitaal en vaardighede, wat nadelig kan wees vir hul ontwikkeling en mededingende voordeel. ‘n Argument kan gemaak word dat die suksesvolle implementering van DW die potensiaal het om besluitneming in KMOs te verbeter en hul mededingende voordeel te verhoog. ’n Semi-gestruktureerde literatuuroorsig is uitgevoer om die verskillende I4.0-tegnologie¨e te verken, asook KMOs en hoe hulle inpas by ekonomie¨e regoor die wˆereld. ’n Gestruktureerde literatuuroorsig is daarna uitgevoer om die probleem te kontekstualiseer, en om die faktore te verstaan wat die suksesvolle implementering van DW in KMOs be¨ınvloed — in ontwikkelende en ontwikkelde lande. Verskeie uitdagings en geleenthede verbonde aan die implementering van DW is ge¨ıdentifiseer. Verder is ’n gebrek aan navorsingsprodukte wat hierdie uitdagings aanspreek ge¨ıdentifiseer, veral binne die konteks van ontwikkelende lande. Die doel van hierdie studie was dus om by te dra tot die suksesvolle implementering van DW in KMOs van ontwikkelende lande. ’n Vereiste spesifikasie hoofstuk is uitgevoer, wat getoon het dat ’n volwassenheid model die mees geskikte navorsingsproduk vir hierdie studie sal wees. Volwassenheid modelle is ontwerp as instrumente wat die gebruiker in staat stel om die huidige vlak van volwassenheid vir verskeie domeine binne ’n organisasie te meet. Hierdie studie het gevolglik die gebruik van ’n volwassenheidsmodel as ’n toepaslike navorsingsproduk vir die implementering van DW in KMOs van ontwikkelende lande ondersoek. In die nastrewing daarvan is die datawetenskap volwassenheidsmodel vir KMOs (DWVMKMOs) ontwikkel. Die model bestaan uit vyf domein komponente, wat beskou kan word as ho¨evlakkategorie¨e van die gegewe domein, wat DW is vir die doel van hierdie studie. Die gekose domein komponente is data, infrastruktuur, mense, bestuur, en beleidsbestuur. Die DWVMKMOs is ontwikkel op grond van ’n grondliggende kennis van huidige literatuur, wat bestaan uit verskeie volwassenheidsmodelle en navorsing met betrekking tot DW en KMOs uit ontwikkelende lande. Die ontwikkeling van die model het ’n streng metodologie gevolg, wat wyd in die literatuur aanvaar word. Hierdie navorsing is dus ’n nie-empiriese, kwalitatiewe studie, wat bestaan uit beide induktiewe en deduktiewe benaderings op ’n ondersoekende wyse. Nadat die DWVMKMOs ontwikkel is, was dit onderhewig aan ’n verifikasieproses wat bestaan het uit onderhoude met vakkundiges. Die terugvoer van die onderhoude is gebruik om ’n tweede iterasie van die model te ontwikkel, wat enige bekommernisse wat deur die vakkundiges na vore gebring is, aangespreek het. Vervolgens was die model onderhewig aan validering en implementering fases, wat die toepaslikheid in terme van toepasbaarheid, uitvoerbaarheid, en bruik baarheid ge¨evalueer het. Die bevindinge van die implementering- en validering fases het getoon dat die DWVMKMOs ’n gepaste instrument is wat by kan dra tot die suksesvolle implementering van DW in KMOs uit ontwikkelende lande.af_ZA
dc.description.versionMastersen_ZA
dc.format.extentxviii, 121 pages : illustrationsen_ZA
dc.identifier.urihttps://scholar.sun.ac.za/handle/10019.1/130682en_ZA
dc.language.isoen_ZAen_ZA
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subject.lcshSmall businessen_ZA
dc.subject.lcshData scienceen_ZA
dc.subject.lcshGross domestic producten_ZA
dc.subject.lcshUCTDen_ZA
dc.titleA maturity model for data science in small and medium-sized enterprises from developing countriesen_ZA
dc.typeThesisen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
rautenbach_maturity_2024.pdf
Size:
6.13 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Masters Degrees (Industrial Engineering)