## 3 Data Management Maturity Model
### Phase 5--Deploy
#### 3.4.3 Evaluation of Results  
The results of the short assessment provide an initial overview of where organizations currently stand. The average maturity level across all industries indicates that the \"short assessed\" organizations have maturity levels between level 3, i.e., managed, and level 4, i.e., established. To maximize the return on investments in digitalization, it is crucial to understand what needs to be achieved to reach level 4, i.e., established. The level definitions are based on ISO 33002 and were supplemented by the project experience of data management experts. It is important to note that the ISO standard levels start at level 0, i.e., incomplete, while this approach begins at level 1, i.e., incomplete. An overall maturity level of 1 indicates that there is no management focus on data topics and no existing vision or strategy regarding data and analytics. In contrast, level 2, i.e., performed, signifies that while there is no overarching vision or strategy, data issues are addressed as needed. At this level, there is silo optimization in data management, and no single point of maintenance is defined. Furthermore, no key performance indicators (KPIs) or reporting mechanisms are established or utilized. At level 3, i.e., managed, where the average of the \"short assessed\" organizations is located, a vision and strategy are defined. Roles and responsibilities, along with guidelines and standards, are mostly established, though they may not always be consistently followed within the organization. KPIs are defined and measured, and system integration is ensured. To advance to level 4 (established), an organization's vision and strategy must be regularly reviewed and adapted. Organizational governance should define and ensure adherence to guidelines and standards. In addition, system integration must not only be ensured but also involve leading systems throughout their lifecycle. A continuous improvement process must also be in place. Achieving a predictive level 5 means that management decision making is data-driven, with investments in new technologies for data management informed by data analysis. Furthermore, the functionalities of the entire organization must be integrated to enable real-time product-service and process-specific data. Quantitative performance management, data analytics, and enterprise application maintenance should be well established. The transition from a predictive to an innovative organization requires a robust data culture that aligns with management strategy and is practiced in daily operations. This also entails self-optimized decision management and business process integration throughout the entire lifecycle. For organizations at level 3 (managed), this indicates that a fundamental foundation is in place for the transition to a data-driven organization, with a clear focus on process improvement [6]. According to the definition of a data-driven organization, \"to become data-driven, not only data and technology capabilities are significant indicators but also organizational capabilities, including fostering a data culture, are vital on the journey to becoming a data-driven organization\" [8]. This means that value is generated from data, as processes, systems, and structures are not only defined but also established, with a continuous improvement process actively in place. The strategy and vision should beregularly reviewed and adapted, which is essential in the digital age with its constant influences and changes, ultimately leading to the establishment of a data culture. For the process dimensions of the model, the strategy, processes, and organization dimensions form the foundational framework that supports the data and architecture dimensions. The operations, support, and innovation dimension becomes increasingly important for achieving higher levels (5 and 6), as data must be leveraged in new and innovative technologies, which requires a certain level of data quality.