Due to the penetration of the industry with digitized and connected components, the amount of data has increased in recent years. More and more companies are implementing data analysis projects in order to be able to utilize this data profitably. The prerequisites necessary for such projects are either ignored or incorrectly evaluated. These prerequisites are the input factors of the data analysis process, such as effective data acquisition and efficient data provision on the one hand and the content of the data in the classical data quality view on the other. If these two facets are not sufficiently developed for a project, it leads not only to time and consequently financial deviations in the implementation, but in the worst case to the failure of the project and to a loss of reputation of data analysis initiatives. Innovative and forward-looking projects in the field of data analytics should be used to develop structures in the company to a level where they can meet future challenges. Maturity models support the evaluation of business processes and their structured improvement. Among numerous existing maturity models for digitization, however, there is none that deals with the focused evaluation of the data analytical process with a focus on its required input factors, such as data management and data quality. The maturity model developed in this thesis should close this gap. The evaluation is based on the CRISP-DM as a generic process model for data analysis. The structure considers common data quality dimensions in order to break down assessment requirements to the level of maturity categories. Six maturity level categories are selected in such a way that practice-oriented recommendations for action can be made in each of them in order to achieve an improvement in the maturity level. The hierarchy of maturity levels is oriented towards the increasing complexity of analysis concepts, the use of which increases company benefits. The maturity model was developed and tested using case studies. In this context a method for a Big Data supported weak point analysis from the classical method case of data analysis was used and the results were presented in a data supported Ishikawa diagram.