Treffer: Big Data Analytics for Financial Decision-Making by Small-and Medium-Scale Enterprises in South Africa.

The world is rapidly and increasingly becoming digital, with over 90% of the data that exists worldwide created since 2010. This process was exacerbated by the COVID-19 pandemic, which saw people being housebound and forced to engage online and, in this way, increased e-commerce. This led to the massive amounts of data that are being added daily. Governments, institutions and big companies mine, refine, store and analyse data, which they use for decisionmaking and as the basis for innovation and increased profitability. Unfortunately, many small- and medium-scale enterprises (SMEs) are unable to take advantage of the big data that is available to them. SMEs play a pivotal role in growing the economy of developing and emerging markets. In South Africa, over 98% of businesses fall under the category of SMEs; they account for over 45% of the country's gross domestic product; and they employ over 60% of the total labour force. SMEs face several challenges that hinder them from appreciating and using big data analytics. These include a lack of data-science skills and insufficient finances for purchasing the necessary infrastructure and employing appropriately skilled personnel for conducting data analytics. The purpose of this study was to develop a road map for SMEs to follow in adopting and implementing big data analytics. The study adopted a sequential mixed method and a pragmatic philosophical stance. The study began by conducting a bibliometric analysis to understand the challenges faced by SMEs concerning big data analytics and to understand what had been studied thus far, by whom and where. A total of 494 articles, books and documents were sourced from the Scopus database for the years 2005 to 2022. VOSviewer and Python were used as tools to mine and analyse the data. The presentation of the data led to the identification of the Power BI tool as potentially useful, based on two studies that recommended the use of this tool by SMEs for big data analysis. Power BI was adopted and used to create a dashboard for analysis. Experiments were conducted on data obtained from a small-scale poultry producer that was selected using convenience sampling. Ten people were used to confirm the usability of the artefact (dashboard) in interviews, after they had been trained on how to use the tool. The interviews were analysed using ATLAS.ti and the results of the different data-analysis methods were triangulated. This study followed the Technological Organisational Environmental (TOE) theory framework as the base for adopting and implementing big data analytics using the Power BI tool. The tool was found to be working and user-friendly to SMEs and to respond to the problems faced by SMEs such as the lack of skills and finances as it is freely available. This study recommends that a collaboration be formed between government departments that support SMEs and institutions of higher learning to work together to help SMEs. SMEs need to be taught about legal factors concerning data management, for example. Future research will focus on the environmental factors concerning big data analytics from the context of SMEs. [ABSTRACT FROM AUTHOR]

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