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In an era of pervasive digitalization, organizations possess unprecedented volumes of data, yet few convert these resources into enduring strategic capabilities that deliver sustainable competitive advantage. This conceptual article synthesizes the literature on data as a strategic resource, information processing, analytics-enabled decision-making, and organizational knowledge conversion to address a critical gap: the mechanisms by which raw data become higher-order capabilities. Drawing on studies, the article introduces the Transforming Information into Strategic Capability Architecture (TISCA) framework. This novel six-layer model explicates the progressive transformation from data possession to capability orchestration and performance reinforcement. The framework highlights the pivotal roles of digital infrastructures, analytics systems, organizational sensemaking, and dynamic feedback loops in turning information into a source of sustainable advantage. By mapping the processes of acquisition, interpretation, integration, deployment, realization, and reinforcement, TISCA offers managers and scholars a practical architecture for designing data-driven capability-building initiatives. Theoretical contributions extend the resource-based view and dynamic capabilities literature by specifying the micro-processes of information-to-capability conversion. Managerial implications focus on the organizational conditions required for information resources to generate long-term competitive differentiation rather than transient operational gains.

Firms increasingly invest in data infrastructure, analytics tools, digital platforms, and specialist talent in the expectation that these resources will improve decision quality, operational effectiveness, innovation, and market performance. Yet the relationship between analytics investment and business value remains uneven, because data availability does not automatically generate strategic action. This article addresses the persistent data-to-value challenge by examining why analytics capability often remains under-converted into realized business outcomes.The central problem is that prior research has frequently examined analytics capability, decision-making, and business value as related but insufficiently integrated domains. Analytics capability explains what firms can potentially know, while business value explains what firms ultimately gain, but the conversion mechanism between the two is often underdeveloped. This article argues that managerial interpretation is the missing link that determines whether analytical outputs become meaningful, trusted, and actionable.The objective of this article is to develop a new conceptual model, the Data-to-Business-Value Conversion Model. The model links analytics capability to business value and competitive advantage through managerial interpretation as the central mediating mechanism. It explains how firms move from data resources and analytical outputs to decisions, organizational actions, value creation, and strategic advantage.The proposed model identifies four connected elements: data and analytics capability, managerial interpretation, business value creation mechanisms, and competitive advantage pathways. It shows that analytics capability provides decision potential, managerial interpretation converts that potential into action, business value emerges through organizational mechanisms, and competitive advantage depends on whether value is embedded in difficult-to-imitate routines. The article contributes a testable framework for future research and a practical logic for managers seeking to improve analytics value conversion.