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Organizations today confront data-saturated markets where exponential growth in digital signals—from social media, IoT devices, customer interactions, and competitive intelligence—creates both unprecedented opportunities and profound challenges for strategic decision-making. Traditional sensemaking processes, rooted in retrospective interpretation of cues, struggle to cope with the velocity, volume, and ambiguity of real-time digital data streams, leading to information overload, signal-noise confusion, and delayed or misguided strategic actions. This theory-development article advances a novel framework of strategic digital sensemaking that explains how organizations can systematically interpret digital signals to reduce uncertainty, filter noise, and translate insights into competitive advantage amid rising market complexity. Drawing on sensemaking theory, dynamic capabilities, and big data analytics literature, the article synthesizes how cognitive, technological, and organizational mechanisms enable effective signal interpretation. It highlights the critical roles of analytics-enabled filtering, collective cognition, and iterative feedback loops in transforming raw digital data into actionable strategic knowledge. Five propositions articulate the relationships among data saturation, interpretation processes, uncertainty navigation, and strategic outcomes. A conceptual model visualizes the dynamic flow from digital signals through interpretation filters and cognition to strategic action, with feedback from outcomes refining future sensemaking. By integrating insights from strategic management, information systems, and organization studies, this manuscript contributes a processual theory that addresses gaps in understanding how firms achieve interpretive agility in data-rich environments. The framework offers actionable implications for managers seeking to build resilient sensemaking capabilities that sustain competitiveness under conditions of high uncertainty and complexity. Ultimately, strategic digital sensemaking emerges not as a static capability but as an ongoing, adaptive organizational practice essential for thriving in data-saturated markets.