Transforming Data Science Teams with the Data Science Lifecycle Process
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The Data Science Lifecycle Process (DSLP) is a revolutionary framework tailored for managing data science projects. This guide will outline its efficiency and adaptability compared to traditional Agile methodologies.
You’ve probably experimented with Agile…
Let’s be honest: many of us have attempted to implement Agile methodologies in our data science projects. However, it's common to witness these efforts unravel — repetitive stand-up meetings, neglected project Kanban boards, and meaningless sprints can lead to a sense of futility.
Why does Agile fail in Data Science?
The Agile framework is primarily designed for software engineering, where a specific end-product is the goal. It focuses on aligning projects with shifting end-user requirements, maintaining a close feedback loop between developers and users, and ensuring communication among team members.
However, this approach often collapses in data science projects because they are fundamentally exploratory and research-driven endeavors. The end-product isn’t defined at the project's outset; instead, it emerges through extensive R&D processes. Only after thorough exploration can the necessary data, preprocessing, and modeling approaches be identified, making Agile less applicable until the final production stage.
Introducing the Data Science Lifecycle Process (DSLP)
In my quest for effective project management in data science, I discovered the Data Science Lifecycle Process (DSLP). This framework consolidates crucial insights from various resources into a cohesive structure that can be seamlessly integrated into GitHub projects or any Kanban-based project management tool.
I have implemented DSLP within my data science team, and it has significantly enhanced our workflow. The advantages we observed include:
- Comprehensive project documentation, encapsulating all design decisions and research in one location.
- Streamlined knowledge transfer, reducing friction during handovers.
- Enhanced collaboration among data scientists.
- Improved project prioritization, minimizing wasted effort on poorly defined initiatives.
- A task-oriented workflow that harmonizes with existing Kanban structures, facilitating the iterative processes typical in Agile — but tailored for data science.
The Five Steps of DSLP
Using my template GitHub Project as an example, DSLP comprises five lifecycle steps: Ask, Data, Explore, Experiment, and Model. Each phase corresponds to a GitHub Issue raised in your data science project.
The following is a brief overview of each step and their respective Issues:
Ask
Ask issues are utilized to define, scope, and refine the value-driven problems your team is addressing. This serves as a live work definition, anchoring all subsequent efforts.
Data
Data issues focus on collaboration for gathering and generating datasets essential for solving the identified problems.
Explore
Explore issues provide quick summaries and insights from exploratory work, enhancing understanding and enabling knowledge sharing among team members.
Experiment
Experiment issues track the various methods employed to tackle a problem and document their outcomes.
Model
Model issues involve the steps necessary to productionize successful experiments, including writing tests and creating deployment pipelines.
Example Project: Detecting Credit Card Fraud
Imagine you are a data scientist at a bank, approached by a subject matter expert (SME) regarding improving credit card fraud detection. After initial discussions, you realize that the project requires formal scoping and documentation.
Creating an Ask Issue
The first step involves establishing an Ask issue to clearly outline the project’s objectives and scope.
As you gather more information, you’ll refine the problem statement and update the Ask issue accordingly. This iterative process ensures a comprehensive understanding of the project as it evolves.
Exploring the Data — Data Issue
Following discussions with the SME, the next step is to create a Data issue to identify and access the necessary datasets for the fraud detection model.
This Data issue will log all relevant activities related to acquiring the necessary data, including any limitations encountered.
The Kanban Board for Data Science
To manage your projects effectively, set up a Kanban board that tracks the progress of tasks.
This tailored Kanban board distinguishes between the different stages of R&D, allowing for a comprehensive overview of project status while facilitating Agile practices like stand-ups and sprint reviews.
Conclusion
The DSLP framework is beneficial for data scientists at all levels. It can be utilized in any project management tool that supports Kanban workflows, not just GitHub.
As data science increasingly becomes an R&D-oriented profession, effective project management, documentation, and audit trails are critical. This framework not only enhances collaboration and efficiency but also prepares teams for the growing regulatory demands in data science.
I welcome your thoughts on this framework, and if you found this article helpful, please share it with your colleagues and give it a clap!