Does Scrum Work for Data Analytics?

Data analytics projects are instrumental in helping organizations make data-driven decisions and gain insights into their operations. However, these projects often face challenges related to changing requirements, tight deadlines, and complex data sets. To address these challenges, many teams are turning to Scrum, an agile framework designed to improve project management and adaptability. But does it really help? Let’s dive into it.

What is Scrum?

‘’Scrum is a lightweight framework that helps people, teams and organizations generate value through adaptive solutions for complex problems.” – Scrum Guide.

Scrum is an agile framework that promotes collaboration, flexibility, and transparency in project management. Scrum defines the roles and responsibilities of each member of a Scrum Team. It has its own artifacts and events, which together enable Transparency, Inspection, and Adaption – the three pillars of Scrum. (Refer to Scrum Guide to learn more)

 

Benefits of Applying Scrum in Data Analytics:

 

• Empirical Approach: Like Data Analytics, Scrum emphasizes executing based on what's known and adjusting plans through iterative delivery and feedback. It encourages teams to experiment and learn from their work.
  
  
• Customer-Centric: Scrum prioritizes delivering customer value, aligning development efforts with customer needs, and gathering constant feedback through practices like Sprint Reviews.
  
  
• Regularity: Scrum's fixed time boundaries establish a regular work cadence, which is beneficial from a human psychology standpoint, and helps maintain a sense of rhythm in the workflow.
  
  
• Accountability: Scrum enables teams to self-govern, promoting higher job satisfaction, productivity, and engagement among team members. This also makes team members feel responsible for their work through practices like Daily Standup and Sprint Reviews.
  
  
• Continuous Improvement: Teams continually inspect their performance, particularly during Sprint Retrospectives, to learn from previous cycles and adapt their processes for better results.

 

 

Challenges in Applying Scrum to Data Analytics:

 

• Time Boxing Issues: Scrum's time constraints can be problematic for data analytics/science teams, as the time required to solve data-related problems is often uncertain and depends on several factors like data quality and complexity.
  
  
• Cultural Hurdles: Scrum's shift from traditional hierarchical structures can be unsettling for many, including team members, data analysts/scientists, and management. Resistance to self-directed work and a focus on teamwork can be cultural challenges.
  
  
• 'Definition of Done' Dilemma: Scrum requires delivering product increments that meet a predefined 'Definition of Done', which may not align with the exploratory nature of data analytics/science, leading to potentially unnecessary constraints.
  
  
• Meeting Overhead: The various Scrum meetings, including Sprint Planning, Daily Scrum, Sprint Review, and Sprint Retrospective, can be viewed as overhead, adding to the workload.
  
  
• Learning Curve: Scrum is recognized as challenging to master, and data analytics/science teams may struggle to understand and implement the methodology effectively.

 

 

Recommendations for Implementing Scrum in Data Analytics:

 

• Provide Comprehensive Training: Organizations should offer extensive training to team members to help them understand and implement Scrum effectively.
  
  
• Prioritize 'Spikes': Create backlog items for research and discovery, allowing for intense study on specific topics alongside product increment ideas.
  
  
• Divide Tasks: Break down data analytics/science tasks into smaller, definable, and estimable pieces to improve predictability.
  
  
• Shorten Sprints: Use shorter sprint cycles to enable more frequent inspections, especially in uncertain and dynamic data analytics/science projects.
  
  
• Relax 'Definition of Done': For exploratory work and POCs, teams can occasionally relax the 'Definition of Done' to avoid excessive constraints on early-stage projects.
  
  
• Renegotiate Work: Remember that the sprint plan is not set in stone; teams can negotiate the scope of the Sprint Backlog when necessary.
  
  
• Build an 'Architectural Runway': If the project requires significant architectural work, consider dedicating initial sprints to develop it, like in Scaled Agile Framework (SAFe).
  
  
• Integrate with CRISP-DM: Combine Scrum with a data science process framework like CRISP-DM to effectively manage data science projects.
  
  
• Appoint a Data Science Scrum Master: Ensure that data science scrum teams have a Scrum Master who understands both Scrum principles and the unique challenges of data science projects.

By implementing these recommendations, when applied to data analytics/science projects, Scrum can provide a structured approach to managing the project, fostering adaptive responses to evolving requirements, and ensuring that stakeholders are continuously engaged in the process. In fact, another Scrum-based framework has already been developed specifically for data science projects called Data Driven Scrum™.