This research examines the adoption of data-driven strategies within German Small and Medium-sized Enterprises (SMEs) and crafts, a sector that remains underexplored despite its substantial economic presence. While large corporations have capitalized on the benefits of Artificial Intelligence, Machine Learning, and Big Data, this study addresses the critical question of whether similar data-driven approaches can be effectively implemented in smaller organizations. Our research aims to bridge the existing gap in literature by focusing on the specific challenges and opportunities that German SMEs face in transitioning towards data-driven decision-making. The methodology employs a systematic literature review combined with the Design Science Research (DSR) approach, which facilitates the development of a practical scoring model and a chronological framework for assessing and enhancing data utilization and decision-making processes. Expected results include the identification of key data-driven artifacts and the establishment of a scoring system that quantifies various data-driven criteria relevant to SMEs. This research will not only contribute to academic knowledge by detailing a tailored approach for SMEs but also provide actionable insights that enhance their strategic and operational decisions, ultimately fostering a culture of innovation and competitive advantage within the German SME sector.