## 3 Data Management Maturity Model
### Phases 2 and 3--Design and Populate
#### 3.2.3. Definition of Dimensions and Capabilities
Other relevant capabilities found in the literature include sponsorship and portfolio management, organizational learning (DDPCDM), roles and responsibilities (DAMA-DMBOK), and culture (digital maturity model based on SCP and DCV). In this dimension, our model aligns well with the most cited capabilities. The data dimension is addressed in models such as DMMM and DREAMY (data management). Data quality is explicitly mentioned in DAMA-DMBOK, CMMI, DCAM, IBM DGMM, Stanford DGMM, and MD3M. Oracle also addresses topics such as data utilization. Our model focuses on more specific capabilities such as data accessibility, data harmonization, data visualization, and data-driven decision making. The IT architecture dimension is explicitly mentioned in DAMA-DMBOK, CMMI, DCAM, and IBM DGMM. Other models include systems (DMMMM), infrastructure (EIM), integration (Dataflux), and technology (DREAMY, digital maturity model based on SCP and DCV, and data-driven organization maturity model). Our model also adds workflow tools to measure technological implementation. The operations, support, and innovation dimension is covered by data operations in DMMM, CMMI, DCAM, and the digital maturity model (based on SCP and DCV). CMMI also includes supporting processes, but innovation and AI use cases are not extensively covered in these models. Our model explicitly addresses data and AI use cases and innovation processes. The results of the literature review confirm that the six renamed pillars are central areas for determining the maturity level of data projects. However, many existing models lack a detailed representation of the various capabilities and the methods for calculating the maturity of each process dimension. For this reason, this paper attempts to identify possible correlations between the process dimensions and capabilities (Sections 3.3.1 and 3.3.2). Overall, it can be stated that the refined model is close to the core principles of the CMMI data management maturity model and supplements these with relevant capabilities (Table 3). While the data management maturity model (CMMI) addresses the capabilities of data management strategy, communication, data management function, business case, and funding in the data management strategy process dimension, the model presented focuses on the strategy and roadmap; the alignment between business and IT strategy, which includes topics such as business case and funding but does not explicitly list them; and the cultural fit when it comes to the topic of a data culture. Communication and funding are assigned to the organization dimension. The organization dimension deals more specifically with the topic of governance and touches not only on processes but also explicitly on roles and responsibilities, data committees, and the general organizational vision. Employees and the topic of enablement and coaching are also addressed. While the CMMI model in the data dimension focuses very strongly on data quality, including the strategy, profiling, and cleansing of data, the proposed model addresses data visualization, data coherency, data accessibility, data storage in terms of data cataloging, and the data architecture picture. In addition, the handling of data is evaluated regarding data-driven decision making. The topic of data cleansing with associated migration is categorized in the processes dimension of the proposed model. The processes dimension covers data operations topics from the CMMI model when it comes to life cycle management and supplements this with process maintenance topics, continuous improvement process, and innovative topics such as automation and digitalization of processes. Both models are similar in the architecture dimension, with the difference that the proposed model also looks at automation and innovative technologies. In the support dimension, the CMMI model focuses on risk and configuration management and deals with topics such as measurement and analyses, process management, and process quality assurance.