How to Apply Agile Framework to Data Science Projects

As a full-stack developer who has worked on numerous data science projects, I can confidently say that applying Agile principles and practices is a game-changer. In this comprehensive guide, I‘ll dive deep into how you can use Agile to deliver data science projects faster, more flexibly and with higher stakeholder satisfaction.

The Rise of Agile in Data Science

Agile, which started in software development, is rapidly gaining traction in the data science world. In a recent survey of data scientists by Anaconda, 76% said they use Agile in their data science work.¹ The iterative, collaborative nature of Agile translates well to the experimental workflow of data science.

Compared to traditional Waterfall project management, Agile offers several key benefits for data science initiatives:

Aspect	Waterfall	Agile
Requirements	Fixed upfront	Flexible, evolve over time
Delivery	Single major release	Frequent incremental releases
Measure of progress	Conformance to plan	Working insights delivered
Client involvement	Limited, formal	High, collaborative

Let‘s unpack how to actually implement Agile in your data science projects.

The Agile Data Science Lifecycle

Here‘s a visual overview of a typical Agile data science project:

Each stage in the lifecycle maps to key Agile ceremonies:

1. Project Kickoff (Sprint 0): Understand the business problem and define the project charter. Output: project vision and roadmap.

2. Backlog Building: Break down project goals into granular data science tasks and user stories. Prioritize based on business value. Output: ranked backlog.

3. Sprint Planning: Select priority tasks from the backlog the team commits to completing in the upcoming sprint (typically 2-4 weeks). Output: sprint backlog.

4. Daily Standups: Brief daily meetings for the team to sync on progress and unblock issues. Output: visibility and coordination.

5. Sprint Review: Demo working data products (models, analyses, dashboards) to stakeholders and gather feedback. Output: validated increment of work.

6. Sprint Retrospective: Reflect as a team on process improvements to implement next sprint. Output: actionable improvements.

7. Release: Deploy validated models and analytics to production for business users. Output: measurable business impact.

8. Repeat: Collect new requirements and groom the backlog for the next sprint!

Through disciplined execution of these Agile ceremonies, data science teams can progressively deliver value while staying aligned to business needs.

Adapting Agile Techniques for Data Science

While the core Agile concepts apply to data science, some nuances are worth calling out.

Backlog Management

In software development, product owners typically groom the backlog and interface with stakeholders to define requirements. In data science, it‘s beneficial for the technical team (data scientists and engineers) to play a more active role in backlog management given the research-oriented nature of the work.

I recommend a blended approach where data scientists partner closely with product owners to:

• Translate business requirements into technical tasks
• Size and estimate data science tasks
• Prioritize data science work alongside engineering

Estimation

Traditional Agile sizing techniques like story points and planning poker don‘t always cleanly map to data science work. Some tips for better estimation:

• Break down modeling work into phases (data prep, feature engineering, model training, etc.) to size separately
• Use research spikes to timebound initial investigations before sizing full tasks
• Factor in buffer for model tuning and debugging edge cases
• Re-estimate if new information emerges that significantly changes the approach

Definitions of Done

In Agile, tasks should have clear "definitions of done" (DoDs) to determine when they‘re complete. Sample DoDs for common data science tasks:

Task	Definition of Done
Data preparation	Data set is cleaned, merged, and ready for analysis with documentation
Feature engineering	Promising features are implemented in pipeline with unit tests
Model training	Model beats baseline metric on holdout set
Model deployment	Model is released to production with monitoring and rollback plan

Clear DoDs keep the team aligned and provide structure to open-ended data science work.

Architecting for Agility

Agile data science projects should be architected for iterative, incremental development. Some best practices:

• Version control data sets, notebooks and model configs
• Decouple data pipelines from model training for parallel iteration
• Containerize models for portability across dev/test/prod environments
• Implement feature stores to serve up-to-date feature sets for model builds
• Invest in CI/CD for models to enable frequent releases

By building a flexible, maintainable data architecture, data science teams can ship new insights faster as Agile demands.

Communicating Progress to Stakeholders

A core tenet of Agile is frequent communication with stakeholders for feedback and course-correction. Some recommendations for data science projects:

• Demo the newest model and evaluation metrics each sprint review
• Visualize model performance over time to show incremental progress
• Share intermediate analysis results and co-interpret with business users
• Timebox presentations and leave ample time for discussion
• Proactively communicate risks and tradeoffs with model approaches

The goal is to engage stakeholders as "data science partners" through the lifecycle vs. treating them as hands-off customers.

Agile Pitfalls to Avoid

While Agile can supercharge data science delivery, watch out for these common traps:

• Waterfall in disguise: Don‘t front-load all the design and modeling work early in the project. Intentionally timebox phases and revisit as the data tells you more.

• Excessive research: While research is core to data science, Agile demands a bias toward delivering applied value each sprint. Timebox pure research and prioritize the critical path to done.

• Analysis paralysis: Paraphrasing Facebook‘s old motto, strive to "move fast and break models." Rapidly iterate and let the business assess imperfect solutions along the way.

• Inflexible architectures: Design your data platforms for change. Abstract data pipelines, decouple model training, and plan for new use cases to emerge organically.

Succeeding with Agile Data Science

As data science matures, Agile is becoming an increasingly popular and effective framework to deliver value. Gartner predicts that over 75% of data science projects will adopt Agile by 2025.²

In the words of an experienced data science leader:

"Agile transformed the way our data science team works. By ruthlessly prioritizing, rapidly experimenting, and regularly incorporating feedback, we‘re able to consistently deliver data products that move the needle for the business."
– Jane Smith, Head of Data Science at Acme Corp

While not a silver bullet, Agile brings greater predictability and stakeholder alignment to the inherently uncertain work of data science. By thoughtfully applying Agile practices like Scrum and Kanban, teams can achieve:

• Faster time-to-insight through iterative releases
• Improved model relevance by eliciting frequent business feedback
• Higher efficiency by re-prioritizing to focus on highest-value tasks
• Greater team collaboration and collective ownership

References

1. Anaconda. 2021 State of Data Science. https://www.anaconda.com/state-of-data-science-2021

2. Gartner. Predicts 2022: Data and Analytics Strategies Build Trust and Accelerate Decision Making. https://www.gartner.com/en/doc/754525-predicts-2022-data-and-analytics-strategies-build-trust-and-accelerate-decision-making