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Current approaches for executing big data science projects-a systematic literature review.

Jeffrey S Saltz1, Iva Krasteva2

  • 1Syracuse University, Syracuse, NY, United States of America.

Peerj. Computer Science
|May 2, 2022
PubMed
Summary

Many big data science projects fail due to process issues, not technical ones. This review highlights the need for better execution frameworks, focusing on workflow and agility, to improve project success rates.

Keywords:
Agile data scienceBig data scienceBig data science workflowsProcess frameworksProject execution

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Area of Science:

  • Data Science
  • Information Systems
  • Project Management

Background:

  • Big data science projects often fail to deliver value due to process and methodology gaps.
  • Lack of mature execution frameworks hinders successful data science project delivery.
  • Project execution processes are critical for realizing organizational value from big data.

Purpose of the Study:

  • To systematically review research on big data science process frameworks.
  • To identify key themes in data science project execution, organization, and management.
  • To explore the activities within a data science project lifecycle and implications for future research.

Main Methods:

  • Systematic literature review of 68 primary studies.
  • Thematic classification of research into six categories.
  • Analysis of current research on data science project execution and methodologies.

Main Results:

  • Workflow and agility are dominant themes, comprising 80% of studies.
  • Workflow approaches primarily adapt existing frameworks like CRISP-DM.
  • Agile approaches are mostly conceptual, with limited empirical evaluation in data science contexts.

Conclusions:

  • Future research should focus on practically achieving the benefits of agility in data science.
  • There is a need to develop integrated workflow and agile frameworks for comprehensive project execution.
  • Improving data science project methodologies is crucial for enhancing organizational decision-making and efficiency.