What factors are causing the increased error rates in NAVEX's PolicyTech document approval workflows during the last month?

Introduction

The increased error rates in NAVEX's PolicyTech document approval workflows over the past month present 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 strategic implications.

I'll approach this problem by first clarifying the context, then ruling out external factors before diving deep into the product ecosystem, metric breakdown, and data analysis. From there, I'll form and validate hypotheses, conduct root cause analysis, and propose a comprehensive resolution plan.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a recent change in the system. Has there been any significant update or deployment to PolicyTech in the last 1-2 months?

Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, there was a minor update. Impact on approach: If yes, I'd focus on change-related hypotheses; if no, I'd look at gradual degradation factors.

• Considering user segments, I'm curious about the distribution of errors. Are we seeing this increase across all user types, or is it concentrated in specific groups?

Why it matters: Helps narrow down if it's a user-specific or system-wide issue. Expected answer: It's affecting enterprise users more than SMB. Impact on approach: If segmented, I'd focus on specific user journey analysis; if universal, I'd lean towards system-wide issues.

• Thinking about the error definition, has there been any change in how we're measuring or logging these errors recently?

Why it matters: Ensures we're comparing apples to apples in our metrics. Expected answer: No changes in error logging or measurement. Impact on approach: If changed, we'd need to recalibrate our baseline; if not, we can trust the historical data.

• Considering external factors, have we seen any significant changes in usage patterns or volume that might explain the increased error rates?

Why it matters: Helps distinguish between system issues and capacity problems. Expected answer: Usage has been steady. Impact on approach: If usage spiked, we'd look at scalability; if steady, we'd focus more on internal system issues.