What factors are contributing to the increased error rate in document comparisons within Litera Compare over the last month?

Introduction

The increased error rate in document comparisons within Litera Compare over the last month is a critical issue that demands immediate attention. As we delve into this product execution problem, I'll employ a systematic approach to identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications.

To tackle this challenge, I'll follow a structured framework that covers issue identification, hypothesis generation, validation, and solution development. This approach will ensure we leave no stone unturned in our quest to resolve the error rate increase and prevent similar issues in the future.

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 Litera Compare in the past month?

Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, there was a major update. Impact on approach: If yes, we'd focus on regression testing and rollback considerations.

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

Why it matters: Helps narrow down potential causes related to user behavior or specific use cases. Expected answer: The issue affects enterprise users more than individual users. Impact on approach: If segmented, we'd investigate unique characteristics of the affected group.

• Thinking about the nature of errors, I'm wondering about the specific types of documents causing issues. Has there been any change in the types or complexity of documents being compared recently?

Why it matters: Document characteristics could be a key factor in comparison errors. Expected answer: There's been an increase in comparing large, complex legal documents. Impact on approach: If true, we'd focus on optimizing for these document types.

• Considering system performance, I'm curious about any changes in infrastructure or load. Have we seen any significant increase in usage or changes in our infrastructure that might impact performance?

Why it matters: System load and infrastructure changes can affect error rates. Expected answer: Usage has increased by 30% in the last month. Impact on approach: If confirmed, we'd look into scaling solutions and performance optimization.

• Reflecting on the error definition, I want to ensure we're all on the same page. Has there been any change in how we define or measure "errors" in document comparisons?

Why it matters: Changes in metrics can sometimes explain apparent performance shifts. Expected answer: No changes in error definition or measurement. Impact on approach: If changed, we'd need to recalibrate our analysis based on the new definition.