Integrated data analytics approaches are increasingly critical for achieving end-to-end supply chain visibility, uncertainty quantification, and effective risk governance in complex operational ecosystems. At a broad level, modern supply chains generate extensive data across sourcing, production, inventory, transportation, and distribution functions. Integrating these heterogeneous data streams enables organizations to transition from fragmented monitoring toward holistic, system-wide visibility and coordinated risk management. Narrowing this focus, uncertainty quantification plays a central role in understanding variability arising from demand fluctuations, supply disruptions, process instability, and external shocks. Statistical modeling, probabilistic forecasting, and machine learning techniques provide complementary tools to measure uncertainty, identify risk drivers, and assess their impact on operational performance. These methods allow organizations to move beyond point estimates and incorporate confidence bounds, scenarios, and likelihood assessments into planning processes. This abstract emphasizes the role of integrated analytics in strengthening supply chain risk governance. By embedding uncertainty-aware insights into governance structures, decision workflows, and performance monitoring systems, organizations can align strategic objectives with operational risk controls. End-to-end analytics supports transparent risk communication, informed escalation, and data-driven mitigation strategies. As a result, integrated data analytics frameworks enable resilient, accountable, and efficient supply chain management in dynamic and uncertain environments.