Introduction
The increased error rate in OneTrust's Data Discovery scans for cloud environments over the past two weeks 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 our product strategy.
I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into our product ecosystem. We'll break down the metric, gather relevant data, form hypotheses, and conduct a thorough root cause analysis. Finally, we'll develop a comprehensive plan to validate our findings and implement solutions.
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 cloud infrastructure update two weeks ago. Impact on approach: If confirmed, we'd focus on the update's impact on scan processes.
Why it matters: Helps isolate whether the issue is cloud-specific or more widespread. Expected answer: No, on-premises scans are unaffected. Impact on approach: We'd narrow our focus to cloud-specific components and configurations.
Why it matters: Identifies if the problem is universal or provider-specific. Expected answer: The issue is more severe with AWS environments. Impact on approach: We'd investigate AWS-specific integrations or recent changes in their APIs.
Why it matters: Ensures we're not dealing with a measurement anomaly rather than an actual performance issue. Expected answer: No changes to error measurement or reporting. Impact on approach: Confirms the issue is with the scans themselves, not our monitoring.
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