The present era, oftentimes referred to as the data age, is characterized by an enormous volume of data across various sectors. Similar to how oil has shaped the industrial age in the 19th century, data are now the crucial resource for gaining competitive advantages. However, harnessing this potential requires thorough analysis and domain knowledge to extract valuable information from these data. To optimally leverage this knowledge, domain experts have to be involved in the entire analysis process. This doctoral thesis introduces the user-centric data analysis approach, empowering domain experts to navigate the full-featured analytical journey, from selecting data sources to data preprocessing, data mining, and reporting - without the need for extensive technical knowledge. This holistic approach encompasses not only a reference model for user-centric data analysis but furthermore includes concepts, prototypical implementations as well as comprehensive evaluations for several phases of the analysis. The user-centric data analysis approach is systematically compared to various state-of-the-art approaches, such as process models or visual analytics, based on six different dimensions. This comparison reveals that, through the introduced approach, domain experts are significantly better integrated into the analysis process, resulting in faster insights and competitive advantages.weiterlesen