Data science is a multifaceted field that integrates statistics, computer science, social science, and other domains to generate valuable insights from data. Despite unprecedented development, many data science projects fail to achieve desired outcomes. This paper presents a work-in-progress systematic literature review of grey literature to explore the opinions of industry practitioners on data science failure. Specifically, this study reviews trade journals, news articles, blogs, and industry reports published from 2018-2023 to identify common data science failure themes outside of traditional academic literature. Initial findings reveal that technical, process, people, financial, and organizational frictions frequently undermine data science projects. Furthermore, risks related to AI governance, ethical considerations, CRM strategies, data quality, access, and team skills also contribute to data science failure. The analysis highlights the contextual nature of “failure,” emphasizing the importance of critical thinking that must align with data science goals and business needs. In short, the results suggest that grey literature provides unique perspectives into data science failure, which can be complementary to peer-reviewed scholarship.

The overarching goal of this research was to gain an understanding of what the data science Reddit online community discussed before, during, and after COVID-19. We used a publicly available Reddit API to harvest the r/datascience subreddit first level post data. We then performed manual annotation to explore the taxonomy of trends and themes discussed by the practitioners who belonged to reddit data science community. Then, we augmented the manually annotated data using a BERT model with topic modeling. In short, the key discussion themes, in order of frequency, were: Education, Jobs, Methods (of data science), Hardware and data collection, Data visualization, and Quality. The Quality theme includes discussions on bias, transparency, and fairness. Hence, a key finding was that there were very few discussions on data science project quality, especially trying to minimize the risk of machine learning bias. As discussions on bias are not yet common, data science teams should proactively identify and address potential questions and concerns that might arise in data science projects, especially the need to increase the team’s focus on potential bias and fairness.