## 3 Data Management Maturity Model
### Phases 2 and 3--Design and Populate
#### 3.2.3. Definition of Dimensions and Capabilities  
Following the initial literature review and the comparison of the data management maturity model (as reference model) and the digital transformation capability maturity model discussed in [PERSON] et al. 2022 [13], it became clear that the scope of the maturity model needed to be expanded from master data to encompass overall data management. This expansion affects the process dimensions, as one of the dimensions was specifically focused on master data. Six consultant experts from the area of data management re-designed an initial draft of the refined maturity map based on project experiences in that domain. As a result, the master data dimension was renamed as the data dimension. Additionally, the operation and support dimension was expanded to incorporate the topic of innovation due to the increasing presence of new technologies in the digital age. Figure 4 illustrates the refined maturity model, highlighting these identified areas of improvement. The following section briefly describes the content of each process dimension. The strategy dimension encompasses all strategic instruments required for the successful implementation of data management. It also covers data strategy and its influence on both the organization's strategic decisions and day-to-day operations. The processes dimension includes the operational maintenance of processes, lifecycle management, and the degree of process automation. The organization dimension addresses the structure of the data organization, including roles, responsibilities, governance, standards, and guidelines for effective data management. The data dimension deals with the definition of data models, objects, their semantics, and nomenclature. This is not limited to master data but applies to various data types. The IT architecture dimension covers the technological implementation and structure of the organization's solution architecture. The operations, support, and innovation dimension covers not only support topics but also innovation, particularly focusing on the degree of innovation and the use of emerging technologies. This is increasingly relevant in the digital age with the rapid development of new technologies. The maturity model aims to address the key elements of a data-driven organization, including digital transformation, data science, data-driven business models, data-driven innovation, and data analytics. The strategy process dimension addresses digital transformation by focusing on corporate strategy and culture. Data science, data-driven innovation, and data-driven business models are covered by content such as data strategy and roadmap, enablement and coaching, innovation strategy and roadmap, and the organizational target picture. Data analytics is included under capability enablement and coaching. The literature review (see Table A1) shows that the strategy dimension is explicitly mentioned in maturity models such as EIM, CMMI, DCAM, Oracle, and digital maturity (based on SCP and DCV). It is also indirectly referenced through categories such as enterprise intent (DMMM), goals and principles (DAMA-DMBOK), and strategic alignment (DDPCM). In the refined model, we also use the term \"cultural fit of strategy\" to encompass both strategic content and its link to organizational and people culture. The processes dimension is explicitly mentioned in EIM, while other models such as DMMM (data operations), Oracle (data maintenance), MD3M (maintenance), and DREAMY  
Figure 4: Design of the maturity map (based on [9]). (Workflow) also cover process-related content. Existing models often describe process activities in general terms, while our model includes not only process maintenance but also lifecycle management, improvement processes, automation, migration, and cleansing activities. The organization dimension is frequently represented as data governance in the literature (e.g., DMMM, DAMA-DMBOK, CMMI, DCAM, and Dataflux).