International Journal of Business, Management and Commerce

Organizations increasingly possess sophisticated analytical technologies yet remain unable to convert data abundance into reliable decision advantage. This conceptual paper integrates research on empirical entrepreneurship, big data analytics capability, data quality, data warehousing, stream analytics, text analytics, multi-criteria decision analysis, and clinical decision support. It advances a unified analytics-to-action capability framework comprising five interdependent layers: data readiness, decision architecture, analytical reasoning, organizational activation, and responsible governance. The framework explains why analytics investments produce contingent rather than automatic performance effects and why the same technical capability yields different outcomes across entrepreneurial, commercial, media, logistics, financial, and healthcare settings. Comparative analysis of small-service-firm, Dell, AMC Networks, UPS, streaming-platform, and clinical decision-support contexts demonstrates that value is created when analytical outputs are timely, decision-relevant, interpretable, and embedded in accountable workflows. Four propositions specify how complementarity among the five layers influences decision quality, organizational agility, and sustained value realization. The analysis also identifies failure modes, including fragmented data, analytics theater, metric gaming, model drift, surveillance concerns, and inequitable automation. The paper contributes to ongoing scholarship by connecting entrepreneurial experimentation with enterprise analytics as a shared learning mechanism and by positioning governance as a productive capability that protects legitimacy while improving the reliability of organizational learning. A research agenda is offered for multilevel, longitudinal, and comparative testing of the framework.