Introduction
CloudFactory's image labeling accuracy rates falling below 95% for the past month is a critical issue that demands immediate attention. This decline in performance could significantly impact our clients' trust and the overall quality of our service. I'll approach this problem systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term and long-term solutions.
Framework overview
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Recent changes often correlate with performance shifts. Expected answer: Yes, we introduced a new AI-assisted labeling tool. Impact on approach: If confirmed, we'd focus on tool integration and training issues.
Why it matters: Establishes a baseline to understand the severity of the decline. Expected answer: Around 97-98% accuracy. Impact on approach: A significant drop would suggest a systemic issue rather than normal variation.
Why it matters: Sudden increases in workload can strain resources and impact quality. Expected answer: 20% increase in volume, similar complexity. Impact on approach: If confirmed, we'd look into scaling issues and resource allocation.
Why it matters: Workforce changes can directly impact labeling quality. Expected answer: Normal turnover, but 15% increase in new hires. Impact on approach: If confirmed, we'd focus on training and onboarding processes.
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