Influence of Big Data Analytics on Decision-Making Processes in Financial Firms in South Africa
DOI:
https://doi.org/10.47672/ajdikm.2350Keywords:
Big Data Analytics, Decision-Making Processes, Financial FirmsAbstract
Purpose: The aim of the study was to assess the influence of big data analytics on decision-making processes in financial firms in South Africa.
Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
Findings: By harnessing vast volumes of structured and unstructured data, these firms can derive valuable insights into market trends, customer behavior, and risk assessment with unprecedented accuracy and speed. This capability enables more informed and data-driven decision-making across various facets of financial operations, including investment strategies, risk management, and customer relationship management. Moreover, big data analytics facilitates the identification of previously hidden patterns and correlations within financial data, enhancing predictive modeling and forecasting capabilities. This proactive approach allows firms to anticipate market shifts, optimize portfolio performance, and mitigate potential risks effectively. Additionally, the adoption of advanced analytics techniques, such as machine learning and artificial intelligence, further enhances decision-making by automating processes and refining predictive accuracy.
Implications to Theory, Practice and Policy: Diffusion of innovation theory, technology acceptance model and resource-based view may be used to anchor future studies on assessing influence of big data analytics on decision-making processes in financial firms in South Africa. In practice, financial firms should establish robust data governance frameworks to ensure data accuracy, privacy, and security. Policymakers should develop and enforce regulatory frameworks that support the ethical and effective use of BDA in financial firms.
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