Track: Doctoral Dissertation Competition

Abstract

The world is becoming more digital per minute. This was exacerbated by the COVID-19 pandemic that saw people being housebound and forced to engage more online and increased e-commerce. This led to the massive data that is being added daily. Over 90% of the data that the world has was created from 2010. Governments, institutions, and big companies mine, refine, store and analyse data. They use this data for decision-making and lead the business to innovation and increased profitability. Many SMEs are left without taking 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 and they account for over 45% to the country’s gross domestic product and they employ over 60% of the total labour force. SMEs have several challenges they are facing that may hinder them from appreciating and using big data analytics such as a lack of data science skills and lack of financing to purchase and higher skilled personnel to do data analytics. The purpose of this study was to develop a road map that SMEs can follow when they want to adopt and implement big data analytics. This study adopted a sequential mixed method and a pragmatic philosophical stance whereby a bibliometric analysis was used to understand the challenges faced by SMEs when adopting big data analytics and to understand what has been studied thus far, by who, and where. A total of 494 articles, books, and documents were sourced from the Scopus database from 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 that was adopted and used to create a dashboard for analysis. The identification was based on two studies that recommended the use of Power BI tools that SMEs can use for big data analysis. Experiments were done on a small-scale poultry producer using convenience sampling. Ten people were used to confirm the usability of the artifact (dashboard) after they were trained and then followed by structured interviews which were analysed using ATLAS.ti. The results were triangulated. This study followed the TOE theory framework as the base to adopt and implement big data analytics using the Power BI tool. The tool was found to be working and user-friendly to SMEs and it responds to the problems faced by SMEs such as lack of skills and finances as it is freely available. This study recommends that there must be a collaboration between government departments that support SMEs and institutions of higher learning to work together to help SMEs. SMEs must be taught about legal factors concerning data management. Future research will focus on the environmental factors concerning big data analytics from the context of SMEs.