The last years have witnessed an increasing shortage of data experts capable of analyzing the omnipresent data and producing meaningful insights. Furthermore, some data scientists mention data preprocessing to take up to 80% of the whole project time. This paper proposes a method for collaborative data analysis that involves a crowd without data analysis expertise. Orchestrated by an expert, the team of novices conducts data analysis through iterative refinement of results up to its successful completion. To evaluate the proposed method, we implemented a tool that supports collaborative data analysis for teams with mixed level of expertise. Our evaluation demonstrates that with proper guidance data analysis tasks, especially preprocessing, can be distributed and successfully accomplished by non-experts. Using the design science approach, iterative development also revealed some important features for the collaboration tool, such as support for dynamic development, code deliberation, and project journal. As such we pave the way for building tools that can leverage the crowd to address the shortage of data analysts.

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Automated headline generation for online news articles is not a trivial task -machine generated titles need to be grammatically correct, informative, capture attention and generate search traffic without being "click baits" or "fake news". In this paper we showcase how a pre-trained language model can be leveraged to create an abstractive news headline generator for German language. We incorporate state of the art fine-tuning techniques for abstractive text summarization, i.e. we use different optimizers for the encoder and decoder where the former is pre-trained and the latter is trained from scratch. We modify the headline generation to incorporate frequently sought keywords relevant for search engine optimization. We conduct experiments on a German news data set and achieve a ROUGE-L-gram F-score of 40.02. Furthermore, we address the limitations of ROUGE for measuring the quality of text summarization by introducing a sentence similarity metric and human evaluation.