Existing research emphasizes the importance of understanding the digital transformation process, yet there remains a significant gap in capturing its dynamic stages and transitions, particularly for small and medium-sized enterprises (SMEs). To address this gap, this study draws on organizational information processing theory and analyzes interview data from six traditional manufacturing SMEs. The findings lead to the development of a data-driven digital transformation process model comprising three distinct stages: (1) data-informed operational excellence, (2) data-synchronized supply chain integration, and (3) data-catalyzed ecosystem innovation. This model also maps the evolution of firm value propositions across these stages: (1) intrinsic value mapping, (2) value chain alignment, and (3) ecosystem value co-creation. Additionally, the study identifies critical preconditions for stage transitions, including supply chain mastery to progress from Stage 1 to Stage 2 and strategic ecosystem analytics integration to advance from Stage 2 to Stage 3. By offering a structured framework for understanding data-driven digital transformation, this study makes a significant contribution to the literature, particularly within the context of traditional manufacturing SMEs.