role of data analytics in driving resilient SME performance in South Africa

Purpose: The COVID-19 pandemic has created unprecedented challenges for businesses worldwide, especially for Small and medium-sized enterprises (SMEs). The pandemic has created new risks and uncertainties that SMEs must navigate to remain operational and competitive. To address these challenges, SMEs need to adopt innovative practices to survive and thrive. Recent studies have shown that data analytics is increasingly becoming a key factor in driving firm performance. Thus, this study aims to empirically assess the importance of data analytics in driving resilient performance. Essentially this paper elucidates the strategic role of data analytics as one of the key components of an artificial intelligence driven world, to drive sustainable firm performance. Methodology: The research employed a distinctive dataset of 450 SMEs in South Africa. Machine learning techniques, particularly Random Forest and Support Vector Regression (SVR), were utilised to model the influence of data analytics on SME performance during the Covid-19 pandemic. This approach facilitated a detailed examination of the correlation between data analytics adoption and organisational resilience during unprecedented circumstances. Results: Data analytics can help SMEs prioritize urgent matters, ultimately improving their performance. Thus, the study recommends analytics software. With the help of analytics software, SMEs can gain valuable insights into critical issues that require immediate attention. By embracing these data analytics solutions, SMEs can effectively leverage their data to generate valuable insights that support decision-making processes. Originality/Relevance: In the context of a developing country during the COVID-19 pandemic, this study addresses a substantial gaps in the literature by concentrating on the role of data analytics in the performance of SMEs. Although prior research has illustrated the significance of data analytics for SMEs in developed countries, this study offers new perspectives on its implementation and influence in South Africa. The use of advanced machine learning techniques to analyze a substantial dataset of SMEs adds methodological rigor to the research. Keywords: SMEs; Data Analytics; Risk; Performance