CC BY 4.0Kling, NicoNicoKlingHaugk, SebastianSebastianHaugkGebauer-Drechsel, HeikoHeikoGebauer-Drechsel2025-10-212025-10-212025https://publica.fraunhofer.de/handle/publica/497589https://doi.org/10.24406/publica-581610.1007/s13132-025-02631-x10.24406/publica-5816Demographic shifts in organisations create an urgent knowledge management challenge: as experienced employees retire, their specialised knowledge must transfer to younger workers. This transition drives small- and medium-sized enterprises (SME) to move from intuitive to data-driven decision-making. We examine how SMEs can leverage data as a strategic knowledge asset during this transformation. Drawing from knowledge-based theory and analysing insights from 200 companies, we develop a practical framework for SMEs’ data-driven evolution. Our research identifies three critical transformation phases: recognising data’s value, building data capabilities, and integrating data into knowledge management. We derive six actionable principles that help SMEs balance structure with adaptability: (1) identifying data’s strategic potential, (2) managing data as a core resource, (3) developing specialised talent, (4) creating efficient coordination mechanisms, (5) nurturing emergent strategies, and (6) implementing flexible data strategies. These principles offer SME managers practical guidance while contributing to academic understanding of data-driven organisations. Our framework particularly helps resource-constrained SMEs navigate their data transformation journey efficiently. Future research should validate these principles quantitatively across different knowledge-intensive sectors.enData-driven organizationKnowledge managementDigital transformationSmall and medium-sized enterprisesStrategic knowledge assetsjournal article