Data Flywheel Design

Problem Statement

An AI writing assistant has strong initial quality from a pre-trained model, but doesn't learn from user interactions. Users edit AI suggestions but that signal isn't captured. Competitors are building data advantages through user feedback loops.

Scope

• •Map all potential data signals from user interactions
• •Design the data collection pipeline with privacy considerations
• •Create the feedback loop architecture
• •Define data quality metrics and monitoring
• •Write a data governance policy

Evaluation Rubric

• •Comprehensiveness of data signal identification
• •Privacy-first design quality
• •Feedback loop architecture soundness
• •Data governance policy thoroughness
• •Strategic thinking about competitive moats