Why has the average processing time for Addepar's data aggregation service increased by 25% in the past two weeks?

Introduction

The recent 25% increase in average processing time for Addepar's data aggregation service over the past two weeks is a critical issue that demands immediate attention. This performance degradation could significantly impact user experience, data accuracy, and overall platform reliability. To address this problem, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both short-term fixes and long-term strategic implications.

Step 1

Clarifying Questions (3 minutes)

• Given the sudden increase, I'm wondering about recent changes. Have there been any significant updates to the data aggregation service or related systems in the past month?

Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, there was a recent update. Impact on approach: If confirmed, I'd focus on the update's impact and potential rollback options.

• Considering the nature of data aggregation, I'm curious about data volume changes. Has there been a notable increase in the amount or complexity of data being processed recently?

Why it matters: Increased data load could explain processing time increases. Expected answer: Data volume has increased by 15% in the last month. Impact on approach: If true, I'd investigate scaling solutions and optimization strategies.

• Thinking about system dependencies, are there any known issues with external data sources or APIs that Addepar relies on?

Why it matters: External dependencies can significantly impact processing times. Expected answer: No known issues with external sources. Impact on approach: If issues are identified, I'd prioritize working with external partners or finding alternative data sources.

• Reflecting on user behavior, have there been any changes in how clients are using the platform, such as increased concurrent users or more complex queries?

Why it matters: Changes in usage patterns can strain the system. Expected answer: User behavior has remained relatively consistent. Impact on approach: If usage patterns have changed, I'd focus on optimizing for new use cases and potentially implementing user education initiatives.