Beyond the Dataset: Understanding Sociotechnical Aspects of the Knowledge Discovery Process Among Modern Data Professionals
Data professionals are among the most sought-out professionals in today’s industry. Although the skillsets and training can vary among these professionals, there is some consensus that a combination of technical and analytical skills is necessary. In fact, a growing number of dedicated undergraduate, graduate, and certificate programs are now offering such core skills to train modern data professionals. Despite the rapid growth of the data profession, we have few insights into what it is like to be a data professional on-the-job beyond having specific technical and analytical skills. We used the Knowledge Discovery Process (KDP) as a framework to understand the sociotechnical and collaborative challenges that data professionals face. We carried out 20 semi-structured interviews with data professionals across seven different domains. Our results indicate that KDP in practice is highly social, collaborative, and dependent on domain knowledge. To address the sociotechnical gap, the need for a translator within the KDP has emerged. The main contribution of this thesis is in providing empirical insights into the work of data professionals, highlighting the sociotechnical challenges that they face on the job. Also, we propose a new analytic approach to combine thematic analysis and cognitive work analysis (CWA) on the same dataset. Implications of this research will improve the productivity of data professionals and will have implications for designing future tools and training materials for the next generation of data professionals.
Cite this version of the work
Anson Ho (2017). Beyond the Dataset: Understanding Sociotechnical Aspects of the Knowledge Discovery Process Among Modern Data Professionals. UWSpace. http://hdl.handle.net/10012/11835