DATA ANALYTICS FOR STRATEGIC BUSINESS DEVELOPMENT: A SYSTEMATIC REVIEW ANALYZING ITS ROLE IN INFORMING DECISIONS, OPTIMIZING PROCESSES, AND DRIVING GROWTH

Abstract

This meta-analysis offers a comprehensive synthesis of empirical evidence on the strategic role of data analytics in business development, with particular emphasis on its contributions to informed decision-making, operational process optimization, financial planning, risk mitigation, and customer-centric growth. Drawing from a dataset of 112 peer-reviewed empirical studies published between 2010 and 2024, the study employs a meta-analytic methodology following PRISMA guidelines to ensure methodological rigor and analytical depth. The research systematically categorizes analytics into descriptive, predictive, and prescriptive models, evaluating their implementation across key business functions including marketing, finance, operations, supply chain, human resources, and strategic management. Findings demonstrate that organizations with high analytics maturity achieve significantly better performance outcomes in terms of profitability, process efficiency, decision quality, and return on investment compared to their lower-maturity counterparts. Firms that integrate analytics across departments and align it with enterprise strategy report the greatest benefits, including improved responsiveness to market dynamics, enhanced customer engagement, and more effective capital allocation. The study further reveals that enabling factors such as executive sponsorship, cross-functional data integration, workforce analytics literacy, and robust governance frameworks are critical to unlocking the full potential of analytics. Comparative evaluation of theoretical models—including the Resource-Based View (RBV), Dynamic Capabilities Theory, Information Processing Theory (IPT), and the Technology–Organization–Environment (TOE) framework—provides insight into the mechanisms through which analytics capabilities translate into sustained competitive advantage. The analysis also identifies gaps related to construct standardization, sectoral applicability, and scalability, offering methodological and strategic recommendations for future research and practice. Ultimately, this study affirms that data analytics is not merely a technological function but a transformative, enterprise-wide capability that drives sustainable value creation, operational excellence, and strategic agility in an increasingly data-driven global economy. The findings contribute to a deeper understanding of how organizations can harness analytics to navigate complexity, enhance performance, and foster innovation.