## 3 Data Management Maturity Model
### Phase 1--Scope
#### 3.1.4 Initial Literature Review  
To capture and evaluate the current state of research, a literature review was conducted to assess the status of existing maturity models related to digital transformation, data management, and data-driven organizations. The aim was to provide an initial overview of which models exist in research and practice and to identify any gaps by also comparing the reference model.  
With the significant increase in data in the 21 st century [16] and the shift toward digital transformation, organizations now require structured approaches to navigate this transition. Maturity models (MMs) are commonly used to reflect an organization's initial status in this process [13]. By 2023, substantial research had been conducted in digital transformation. However, \"most studies focus primarily on the overall process rather than on how companies develop a strategy to effectively utilize their data for Digital Transformation\", and \"maturity models have been largely neglected\" [13].  
Since MMs are still in the early stages of development, and only 27 percent of models have been published in academic journals, [PERSON] and [PERSON] [8] proposed the digital transformation capability maturity model (DX-CMM) in 2021 to address this gap. They evaluated 18 MMs based on criteria such as \"complete, clear, unambiguous, objective, impartial, consistent, repeatable, comparable, and representative\" and developed DX-CMM as a holistic model applicable across all industry sectors. The model encompasses two dimensions--processes and capabilities--that were already applied in a case study. The model identifies an organization's maturity level in digital transformation and highlights opportunities for improvement.  
Since the work of [PERSON] and [PERSON] in 2021, the field of digital transformation has continued to evolve. [PERSON] and [PERSON] [11] introduced the MD3M maturity model, which focuses on master data. Their work provides an overview of existing master-data management models and developed a model that can assess an organization's maturity in master-data management. The model covers five key topics: data model, data quality, usage and ownership, data protection, and maintenance. However, this model is not classified as an MM for strategic guidance in digital transformation, and it has not yet been widely adopted in practice. This reinforces the need for a practical, domain-specific model that supports assessments in digital transformation.  
Another work by [PERSON] et al. [6] introduced a multidisciplinary assessment approach for data-driven organizations, called the data-drivenness process capability determination model (DDPCM). This model was validated for its applicability and usability through a multiple-case study involving two organizations. However, it has not yet been tested in small- and medium-sized enterprises (SMEs), and its practical applicability across all industry sectors remains to be demonstrated.  
The need for a data management maturity model has also arisen in the automotive industry, which led to the presentation of the pilot draft of the data management SPICE assessment by the intacs group in 2022 [17]. Data management SPICE defines the fundamental business processes of data management by establishing a process assessment model (PAM) for the maturity of all types of data and data management processes within organizations.  
In the work of [PERSON] et al. (2013), a comparison was made between the data management strategy maturity model, which is used as reference model in this work, and the digital transformation capability maturity model, based on the criteria outlined in ISO 33020.