Data-Centric Fashion: A Governance Framework for Trend Forecasting and Creative Decision-Making

This paper proposes a data-centric approach to fashion design by framing trend forecasting and creative decision-making as a problem of digital governance. Rather than treating data as a technical input or analytical output, the study conceptualizes data as a human-centred material that actively shapes design processes, strategic choices, and innovation dynamics within the fashion industry. The research introduces a governance framework that integrates data-driven systems, artificial intelligence, and design thinking to support more transparent, adaptive, and informed decision-making across the fashion value chain. By repositioning trend forecasting as a design problem, the framework addresses the limitations of intuition-based practices and fragmented data usage that often lead to misaligned creative and operational outcomes. The proposed model contributes to contemporary debates on data-centric design and digital transformation by articulating how governance structures can mediate the relationship between data, creativity, and human judgement. Although grounded in the context of fashion, the framework offers broader implications for design-led industries seeking to balance data intelligence with human-centred innovation