Measuring ROI of data and analytics programs: A framework for enterprise impact

The use of data and analytics (D and A) in organizational contexts ranging from driving strategic decision-making to optimizing operations and creating sustainable competitive advantage has become a defining feature of the digital era. Global spending on analytics, AI, and big data platforms is projected to surpass USD 300 billion by 2030, reflecting the strategic importance of such initiatives. Despite these investments, one of the most persistent challenges for executives and practitioners is accurately measuring the return on investment (ROI) of D and A programs. Unlike traditional IT or capital projects, analytics initiatives generate both tangible and intangible outcomes, often with delayed realization, interdependencies across business units, and difficulties in attribution. This review builds upon existing scholarship and practitioner evidence to examine current models and practices of measuring ROI in enterprise D and A. It highlights gaps in approaches that focus too narrowly on technical deliverables such as the number of dashboards deployed without sufficiently linking them to enterprise-level outcomes. To address these gaps, a multi-layered theoretical model is proposed, integrating the resource-based view (RBV), analytics maturity models, and strategic alignment theory. This model couples’ input–process–output–outcome dynamics with organizational enablers such as leadership, culture, and governance, ensuring that analytics success is assessed not only in financial terms but also in terms of innovation, risk mitigation, decision quality, and cultural transformation. Empirical synthesis shows that ROI in analytics varies across industries and maturity levels. Organizations at descriptive or diagnostic maturity levels report modest returns (12–18%), while those advancing to predictive and prescriptive analytics achieve stronger ROI (29–35%). Cognitive/AI-driven organizations report the highest returns (above 40%), though these are highly contingent on governance and alignment. Variance across industries is also evident: banking and retail lead in reported returns due to high data intensity and competitive pressures, while healthcare and manufacturing face challenges from regulation, legacy systems, and cultural inertia. By addressing conceptual, empirical, and practical gaps, this research contributes to both theory and practice. Theoretically, it integrates previously fragmented approaches into a single, adaptive framework for ROI measurement. Empirically, it validates the model with evidence from large-scale surveys and case studies. Practically, it provides executives with actionable guidance for linking D and A investments to strategy, prioritizing portfolios, and benchmarking outcomes. The study concludes with a forward-looking agenda, calling for the incorporation of causal inference, decision-centric evaluation, and ESG-linked outcomes in future ROI frameworks.