What is Scrum for Data Science?
Scrum for Data Science applies the Scrum framework to AI and ML projects with adaptations for experimentation, model iteration, data exploration, and performance-driven development. Modified ceremonies focus on experiment results, model performance trends, and data quality improvements rather than traditional software features.
This glossary term is currently being developed. Detailed content covering implementation approaches, best practices, common challenges, and business applications will be added soon. For immediate assistance with AI project management, please contact Pertama Partners for advisory services.
Scrum for data science provides the structured cadence that prevents AI projects from drifting into open-ended research exercises without delivering business value. Teams adopting adapted Scrum frameworks deliver production models 45% faster than unstructured approaches while maintaining higher stakeholder satisfaction throughout development. The regular sprint reviews also build organizational AI literacy as business stakeholders observe and understand the iterative nature of model development.
- Modify sprint planning to include experiment design and data exploration tasks
- Adapt sprint reviews to showcase model performance improvements and experiment insights
- Use retrospectives to improve experiment tracking, collaboration, and model deployment processes
- Define sprint goals around model performance milestones, not just feature delivery
- Maintain a backlog that balances model improvements, data quality, and infrastructure work
- Allocate capacity for unexpected data issues and experimental dead-ends
- Modify sprint commitments to include experiment success criteria rather than feature completion guarantees, acknowledging the inherently uncertain nature of model development.
- Assign product owner responsibilities to someone who understands both business requirements and data science constraints to prevent unrealistic sprint planning.
- Run two-week sprints instead of longer cycles to force frequent experiment evaluation and pivot decisions before teams invest excessive effort in unproductive directions.
- Modify sprint commitments to include experiment success criteria rather than feature completion guarantees, acknowledging the inherently uncertain nature of model development.
- Assign product owner responsibilities to someone who understands both business requirements and data science constraints to prevent unrealistic sprint planning.
- Run two-week sprints instead of longer cycles to force frequent experiment evaluation and pivot decisions before teams invest excessive effort in unproductive directions.
Common Questions
How does this apply to AI projects specifically?
AI projects have unique characteristics including data dependencies, model uncertainty, and iterative development cycles that require adapted project management approaches.
What are common challenges with this in AI projects?
Common challenges include managing stakeholder expectations around AI capabilities, balancing exploration with delivery timelines, and maintaining project momentum through experimentation phases.
More Questions
Various tools and frameworks can support this practice. Consult with project management experts to select approaches suited to your organization's AI maturity and project complexity.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
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