Data Science Project Barriers—A Systematic Review

Natan Labarrère, Lino Costa and Rui M. Lima ()
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Natan Labarrère: Algoritmi Research Center/LASI (Associate Laboratory for Intelligent Systems), Department of Production and Systems, School of Engineering, University of Minho, 4800-058 Guimarães, Portugal
Lino Costa: Algoritmi Research Center/LASI (Associate Laboratory for Intelligent Systems), Department of Production and Systems, School of Engineering, University of Minho, 4800-058 Guimarães, Portugal
Rui M. Lima: Algoritmi Research Center/LASI (Associate Laboratory for Intelligent Systems), Department of Production and Systems, School of Engineering, University of Minho, 4800-058 Guimarães, Portugal

Data, 2025, vol. 10, issue 8, 1-19

Abstract: This study aims to identify and categorize barriers to the success of Data Science (DS) projects through a systematic literature review combined with quantitative methods of analysis. PRISMA is used to conduct a literature review to identify the barriers in the existing literature. With techniques from bibliometrics and network science, the barriers are hierarchically clustered using the Jaccard distance as a measure of dissimilarity. The review identified 27 barriers to the success of DS projects from 26 studies. These barriers were grouped into six thematic clusters: people, data and technology, management, economic, project, and external barriers. The barrier “insufficient skills” is the most frequently cited in the literature and the most frequently considered critical. From the quantitative analysis, the barriers “insufficient skills”, “poor data quality”, “data privacy and security”, “lack of support from top management”, “insufficient funding”, “insufficient ROI or justification”, “government policies and regulation”, and “inadequate, immature or inconsistent methodology” were identified as the most central in their cluster.

Keywords: data science project; project failure; barriers; clustering (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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