CC BY-NC-ND 4.0Kutzias, DamianDamianKutziasDukino, ClaudiaClaudiaDukinoKötter, FalkoFalkoKötterKett, Holger JoachimHolger JoachimKett2023-03-222023-03-222023https://publica.fraunhofer.de/handle/publica/437853https://doi.org/10.24406/publica-107910.5220/001189520000339310.24406/publica-1079When adopting data science technology into practice, enterprises need proper tools and process models. Data science process models guide the project management by providing workflows, dependencies, requirements, relevant challenges and questions as well as suggestions of proper tools for all tasks. Whereas process models for classic software development have evolved for a comparably long time and therefore have a high maturity, data science process models are still in rapid evolution. This paper compares existing data science process models using literature analysis, and identifies the gap between existing models and relevant challenges by performing interviews with experts.enData ScienceProcess ModelsMethodologyProject ManagementArtificial Intelligenceconference paper