SCRUM ACTIVITY

THE PROBLEM

Scrum is an effective agile methodology that provides team collaboration, independent learning, and a maintained level of support. The Business Intelligence department has been able to create a structure that allows project managers to have significant discussions/meetings with customers through the project journey, developers have specifications that are detailed, resources are identified, and they are able to work according to their assigned capacity level, and finally workloads & performance levels are easier for managers to oversee on DevOps.

However, such departmental change in strategy is highly reliant on meticulous planning, unfortunately this requires time which wasn’t on the department’s side. Fast-paced solutions were needed to support the circumstances created by the pandemic, nevertheless long-term solutions were also needed to support potential circumstances created by other factors, i.e., short-staffing, IT Infrastructure, and departmental restructuring in teams, methods, positions, and resources.

THE PRIMARY QUESTION

With the adoption of Scrum methodologies, projects are organised into sprints, each with a maximum duration of 10 working days. Throughout all past, present, and future sprints, the following data remains consistent: ID, Title, Status, Assigned To, Area, Iteration, Original Estimate, Completed, Created Date, Closed Date, and Activity Date. Projects, referred to as 'User Stories,' are broken down into manageable 'Tasks' by product owners or developers. This approach enables capacity measurement and structured task execution. Both User Stories and Tasks include the above-listed constants, forming the foundational dataset for the Scrum Activity Dashboard project.

A primary question driving this project was: How effective are approach, capacity, and productivity in determining the outcome of a sprint?

BREAKING DOWN THE PRIMARY QUESTION

To address the primary question, I analysed iterative activities, focusing on those with the highest and lowest task/project completion rates. This analysis identified key factors influencing capacity, productivity, and approach, shedding light on activities unique to specific sprints. The comparative insights provided answers to several sub-questions:

  • Were capacity estimates more accurate in one sprint than another?

  • What were the levels of underestimation and overestimation?

  • How much work was completed within the sprint versus outside it?

  • How much capacity was carried over from the previous sprint