Case Study: Adapting Scrum for Data Analytics Teams

Brya K. Patterson, Scrum Master & Agile Practitioner, Complaint Insights Case Study (2024–2026)

March 1, 2026 Brya K. Patterson

This case study explores the adaptation of the Scrum framework for a specialized Data Analytics team, moving away from traditional software engineering "product" delivery toward a high-velocity, insight-driven workflow. It documents a two-phase transformation that began in 2024, focusing on balancing leadership requirements for visibility and prioritization with the team's need for autonomy and psychological safety. The narrative details how standard Scrum events were consolidated into a 1.5-hour "Team Sync" to maximize focus time, and how modern tooling—including Jira automation and AI-driven synthesis—was leveraged to provide transparent "receipts" of work for senior stakeholders.

Neural Network: NBA Draft Combine Predictions

Brya Patterson, et al., Summer 2025, University of Washington, MS Information Management (MSIM)

August 19, 2025 Brya K. Patterson

This report investigates whether NBA Draft Combine performance metrics can predict draft outcomes. Utilizing a comprehensive dataset (2000–2025), we applied exploratory data analysis (EDA) and neural network modeling to evaluate the predictive weight of "pure athleticism" versus historical selection trends. While physical metrics provide a baseline, our findings suggest that the modern NBA draft process increasingly prioritizes multi-dimensional skills not captured by combine drills alone. Across three different model families accuracy stayed in the 56–60% range on past seasons and resulted in many false positives at the 0.50 threshold. There were indicators that only the lane-agility drill contributed to consistent signals. These results point to a data ceiling rather than a tuning issue: draft decisions depend on factors the combine does not measure. A more holistic dataset should raise the ceiling.