Joshua Holstein

Motivation:

As data grows in volume and complexity, understanding its relationship to real-world applications becomes increasingly crucial for businesses [1]. This complexity requires the integration of diverse expertise to ensure data is not only analyzed correctly but also applied effectively. Decision-makers need to align insights with organizational strategies, necessitating data interpretations that are clear and actionable. Domain experts provide essential context, making the data relevant and applicable to specific fields such as finance or healthcare. Method experts, or data scientists, handle the technical challenges, processing and modeling data to unearth valuable insights. However, bridging the communication gap between these roles remains a significant challenge due to their differing perspectives, skills and experience [2, 3].

Objectives:

To approach the communication gap, this thesis aims to explore the interplay between decision-makers, method experts, and domain experts in the context of data understanding. Using different qualitative research methods like literature review, interviews a framework should be developed and test to close the communication gap.

We look forward to receiving your application because you:

- You are interested in the field of machine learning
- You are highly motivated to work on recent real-world problems in a self-organized and goal-oriented working mode and you bring in own ideas
- You are open-minded and don’t hesitate to get in touch with professionals, e.g., throughout an interview study
- Very good English skills as the thesis will be written in English

Details

 - Start: immediately 

 - Duration: 6 months

 - Location: up to you

We offer you a challenging research topic, close supervision, and the opportunity to develop practical and theoretical skills. If you are interested, send your CV, transcript of records, and a brief letter of motivation to Joshua.Holstein@kit.edu.

References:

[1] Gerhart, N., Torres, R., & Giddens, L. (2023). Challenges in the Model Development Process: Discussions with Data Scientists. Communications of the Association for Information Systems, 53, 591-611. https://doi.org/10.17705/1CAIS.05325

[2] Aaltonen, A., Alaimo, C., Parmiggiani, E., Stelmaszak, M., Jarvenpaa, S. L., Kallinikos, J., & Monteiro, E. (2023). What is Missing from Research on Data in Information Systems? Insights from the Inaugural Workshop on Data Research. Communications of the Association for Information Systems, 53, 475-490. https://doi.org/10.17705/1CAIS.05320

[3] Vial, G., Cameron, A.-F., Giannelia, T., & Jiang, J. (2023). Managing artificial intelligence projects: Key insights from an AI consulting firm. Information Systems Journal, 33(3), 669–691. https://doi.org/10.1111/isj.12420