Quantifying Data-Driven SMEs: Scoring Factors and a Timeline for Better Decisions

This research builds on our earlier findings regarding the challenges SMEs, particularly in the crafts sector, face when adopting data-driven strategies. While large enterprises successfully utilize AI and data technologies, SMEs often encounter significant barriers to implementation. Building on these insights, this study identifies critical factors in the data-driven domain that can significantly enhance decision-making in German SMEs. The proposed scoring model aims to empower SMEs by enabling them to assess their data capabilities, identify weaknesses, and implement targeted improvements. Through a systematic literature review, the study highlights the most influential factors shaping data-drivenness in SMEs. Using the Design Science Research (DSR) methodology, we developed a refined scoring model and a chronological framework that integrate these factors to guide and improve data utilization. This research ultimately contributes to a deeper understanding of how SMEs can adopt data-driven strategies to foster innovation, optimize operations, and remain competitive in a dynamic market environment.