Data Science Failure: A Literature Review

Sucheta Lahiri, Jeff Saltz

Research output: Chapter in Book/Entry/PoemConference contribution


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.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE International Conference on Big Data, BigData 2023
EditorsJingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9798350324457
StatePublished - 2023
Event2023 IEEE International Conference on Big Data, BigData 2023 - Sorrento, Italy
Duration: Dec 15 2023Dec 18 2023

Publication series

NameProceedings - 2023 IEEE International Conference on Big Data, BigData 2023


Conference2023 IEEE International Conference on Big Data, BigData 2023


  • big data
  • data science
  • failure
  • grey literature
  • machine learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality


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