A Systematic Literature Review of Data Analytics Methods Used in Global Corporate Investment Strategies
Abstract
Corporate investment strategies are increasingly enhanced by data analytics methods, including artificial intelligence (AI), machine learning (ML), and big data analytics. These technologies reshape financial decision-making by enabling faster forecasting, more accurate risk assessment, and optimized capital allocation. Across industries and regions, analytics tools also support strategic agility and alignment between data capabilities and business goals. Based on secondary data, this dissertation investigates how data analytics methods enhance global corporate investment strategies, addressing the lack of integrated, cross-sectoral understanding of how analytics capabilities, governance, and ESG (environmental, social, and governance) considerations jointly shape investment decision-making. A systematic literature review (SLR) was conducted following PRISMA 2020 guidelines and Braun and Clarke’s (2019) six-phase reflexive thematic analysis framework. A total of 25 peer-reviewed articles published between 2015 and 2025 were analysed to synthesise interdisciplinary insights across sectors. The review identifies AI/ML, ESG integration, and data governance as critical enablers of corporate investment strategy, highlighting how these technologies shape analytics-driven decision-making. Organisational factors – including leadership commitment, digital infrastructure, cultural readiness, and regulatory alignment – emerged as pivotal success factors, in translating analytics…
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