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Data Science Project Barriers—A Systematic Review

Author

Listed:
  • 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)

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.

Suggested Citation

  • Natan Labarrère & Lino Costa & Rui M. Lima, 2025. "Data Science Project Barriers—A Systematic Review," Data, MDPI, vol. 10(8), pages 1-19, August.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:8:p:132-:d:1728330
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    References listed on IDEAS

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    2. Neal R. Haddaway & Matthew J. Page & Chris C. Pritchard & Luke A. McGuinness, 2022. "PRISMA2020: An R package and Shiny app for producing PRISMA 2020‐compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis," Campbell Systematic Reviews, John Wiley & Sons, vol. 18(2), June.
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    4. Jeffrey Saltz & Ivan Shamshurin & Colin Connors, 2017. "Predicting data science sociotechnical execution challenges by categorizing data science projects," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(12), pages 2720-2728, December.
    5. Fen‐May Liou & Chien‐Hui Yang, 2008. "Predicting business failure under the existence of fraudulent financial reporting," International Journal of Accounting & Information Management, Emerald Group Publishing Limited, vol. 16(1), pages 74-86, June.
    6. Xiunian Zhang & Jasmine Siu Lee Lam, 2019. "A fuzzy Delphi-AHP-TOPSIS framework to identify barriers in big data analytics adoption: case of maritime organizations," Maritime Policy & Management, Taylor & Francis Journals, vol. 46(7), pages 781-801, October.
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