What factors are contributing to the unexpected 30% increase in data processing time for VideoAmp's TV attribution reports this month?

Introduction

The unexpected 30% increase in data processing time for VideoAmp's TV attribution reports this month presents a critical challenge that demands immediate attention. This issue not only impacts operational efficiency but also has potential ripple effects on client satisfaction and the company's competitive edge in the TV attribution market. 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.

My analysis will follow a structured framework, beginning with clarifying questions to establish context, followed by a thorough examination of potential external factors. We'll then delve into the product's user journey, break down the metric in question, gather and prioritize relevant data, form hypotheses, conduct root cause analysis, and finally propose validation methods and next steps.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent system update. Has there been any significant change to the data processing pipeline in the last month?

Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, there was a minor update to the data ingestion system. Impact on approach: If confirmed, we'd focus on the new system components first.

• Considering the scale of the increase, I'm wondering about data volume changes. Has there been a substantial increase in the amount of data being processed compared to previous months?

Why it matters: Data volume directly impacts processing time. Expected answer: There's been a 15% increase in data volume. Impact on approach: We'd need to investigate if the system is scaling appropriately.

• Given the nature of TV attribution, I'm curious about seasonality. Are we in a period of higher TV viewership or advertising activity?

Why it matters: Seasonal patterns could explain increased data processing demands. Expected answer: It's not a typically high season for TV advertising. Impact on approach: We'd shift focus from seasonal factors to internal system issues.

• Considering the complexity of attribution models, I'm thinking about potential changes in methodology. Have there been any recent adjustments to the attribution algorithms or models?

Why it matters: Algorithm changes can significantly impact processing time. Expected answer: No recent changes to the core attribution models. Impact on approach: We'd focus more on infrastructure and data pipeline issues.

• Reflecting on system health, I'm concerned about potential hardware issues. Have there been any reported problems with the servers or infrastructure used for data processing?

Why it matters: Hardware issues can cause unexpected performance degradation. Expected answer: No major hardware issues reported recently. Impact on approach: We'd prioritize software and data-related factors in our investigation.