Introduction
The sudden 30% decrease in data integration throughput for Arcadia io's claims processing module last week is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term fixes and long-term implications for the product.
I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into the product's user journey and metrics. From there, I'll form data-driven hypotheses, conduct root cause analysis, and propose a comprehensive resolution plan.
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 issues. Expected answer: Yes, there was a minor update. Impact on approach: If yes, we'd focus on change management and rollback procedures.
Why it matters: Unusual data volumes can strain system resources. Expected answer: No significant changes in data volume. Impact on approach: If no, we'd look more closely at system-level issues.
Why it matters: Increased data complexity can slow down processing. Expected answer: Some new fields were added for regulatory compliance. Impact on approach: If yes, we'd investigate data schema and processing logic.
Why it matters: Integration issues can significantly impact throughput. Expected answer: No known issues with connected systems. Impact on approach: If no, we'd focus more on the module itself rather than its integrations.
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