@inproceedings{NeiferLawoEsau2021,
  author    = {Thomas Neifer and Dennis Lawo and Margarita Esau},
  title     = {Data Science Canvas: Evaluation of a Tool to Manage Data Science Projects},
  series = {Proceedings of the 54th Hawaii International Conference on System Sciences, January 4-8, 2021},
  publisher = {ScholarSpace},
  isbn      = {978-0-9981331-4-0},
  doi       = {10.24251/HICSS.2021.657},
  url       = {https://nbn-resolving.org/urn:nbn:de:hbz:1044-opus-52893},
  pages     = {5399 -- 5408},
  year      = {2021},
  abstract  = {Data emerged as a central success factor for companies to benefit from digitization. However, the skills in successfully creating value from data – especially at the management level – are not always profound. To address this problem, several canvas models have already been designed. Canvas models are usually created to write down an idea in a structured way to promote transparency and traceability. However, some existing data science canvas models mainly address developers and are thus unsuitable for decision-makers and communication within interdisciplinary teams. Based on a literature review, we identified influencing factors that are essential for the success of data science projects. With the information gained, the Data Science Canvas was developed in an expert workshop and finally evaluated by practitioners to find out whether such an instrument could support data-driven value creation.},
  language  = {en}
}