Integrating Big Data Architectures and AI-Powered Analytics into Mergers & Acquisitions Due Diligence: A Theoretical Framework for Value Measurement, Risk Detection, and Strategic Decision-Making

Abstract

This article articulates a comprehensive, publication-ready theoretical framework that integrates foundational concepts from big data management, social media dynamics, Internet of Things (IoT) proliferation, and contemporary artificial intelligence (AI) architectures to reconceptualize due diligence practices in mergers and acquisitions (M&A). The work synthesizes seminal and contemporary contributions that frame the data landscape—particularly the three Vs of volume, velocity, and variety (Laney, 2001)—and contextualizes them within socio-technical shifts such as Web 2.0 (O’Reilly, 2005) and the growth of digitally mediated user-generated content (Kaplan & Haenlein, 2010). Drawing on comparative system analyses of large-scale data processing platforms (Ahmed et al., 2020) and theoretical histories of computing and data (Stern, 1981; DiNucci, 1999; Diebold, 2012), the paper proposes a layered, modular methodology for AI-enhanced diligence that explicitly addresses valuation theory (Koller et al., 2020), empirical approaches to machine learning in transactional contexts (Sutskever et al., 2011; Child et al., 2019), and ethical and governance concerns (Harris & Martinez, 2021). The paper advances a multi-stage model for M&A due diligence: Data Acquisition and Provenance, Scalable Processing and Feature Engineering, Domain-Specific Predictive Modeling, Interpretability and Explainability, and Decision Integration and Valuation Adjustment. Each stage is elaborated in rich theoretical detail, with explicit attention to methodological choices, potential failure modes, counter-arguments, and governance responses. The Results section offers descriptive findings derived from thought experiments and established empirical patterns from the cited literature; these findings are translated into prescriptive recommendations for practitioners and policy implications for corporate governance. Limitations are discussed with candor—particularly the tensions between predictive opacity, data quality constraints, and organizational readiness. The article concludes by outlining a research agenda and practical action points for operationalizing AI-driven due diligence while preserving analytical rigor, legal defensibility, and ethical accountability.