## 4 Discussion, Limitations, and Conclusions  
Many existing maturity models lack a holistic approach applicable across industries. To ensure broad usability, capability terms must be generic, while their interpretation should be tailored to specific organizations. The model in this study was refined using a literature review, industry survey, and short assessment. The short assessment yielded an average maturity level of 3.61, indicating a transition between \"managed\" and \"established\". The lowest levels were recorded in the strategy and organization dimensions, highlighting challenges such as weak data culture, insufficient data literacy, and unclear roles. Despite significant digitalization investments, only 15 percent of surveyed organizations use data for decision making, indicating a gap between investment and realized benefits. An average maturity of 3.39 in the organization dimension further underscores vulnerabilities. Only 30 percent of organizations considered themselves as data-literate, and 20 percent reported having clear role definitions. These findings emphasize the need for continued digitalization and the transition to data-driven operations. The refined maturity model aims to address these gaps. The literature review confirms the model's relevance but highlights a lack of detail in capability levels and maturity calculations. The proposed model tries to incorporate the key aspects of data-driven organizations, including digital transformation, data science, and data analytics. However, it has some limitations. First, the model's scope is restricted by reliance on a reference maturity model and a focus on data management, limiting stakeholder involvement to industry and consulting experts. Second, there are several potential confounding factors in the correlation analysis that could have influenced the results of the studies. The small number of participants reduced the statistical significance of the results. With a larger sample, more stable and reliable correlations could be found. Nevertheless, the survey (\(n=20\)) and short assessment (\(n=31\)) included only highly knowledgeable participants, ensuring opinion saturation despite a small sample size. Furthermore, 40 percent of the participants came from the automotive industry, which means that the results may not be fully transferable to other industries. Industry-specific differences could distort the correlations. In addition, a mix of small- and medium-sized companies (35 percent) and very large corporations (25 percent) could lead to a distortion, as larger companies tend to have more resources for digitalization and data management. The study only provides a first overview and cannot prove any causal relationships over time. Changes in digitalization strategies may only become noticeable after several years. External influences such as the economic situation, regulatory requirements, or technological developments were not taken into account but could have a significant influence on digitization decisions. Response bias was not analyzed in detail. Participants could have given socially desirable answers, especially regarding questions about data strategy or digitalization. The choice of scale (1-7) was intended to avoid participants often choosing the center, but they could still have tended to give moderate answers, which could influence the results. Third, the model is at an early validation stage. The validation focuses is on capabilities rather than practical application. The short assessment provided initial feedback, but subjectivity and participant roles may have influenced results. Future validation will refine the assessment methods. Fourth, the scalability of the short assessment remains theoretical and requires further evaluation in the maintain phase. The current weightings are based on expert input rather than on practical validation. Future iterations will involve extended industry testing in various sectors (e.g., automotive, aviation, consumer goods, pharma, and media).