What caused the sudden spike in failed data quality checks for Monte Carlo's data catalog feature last week?

Introduction

The sudden spike in failed data quality checks for Monte Carlo's data catalog feature last week 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 data catalog product.

I'll approach this problem by first clarifying the context, then ruling out external factors before diving deep into the product mechanics, metric breakdown, and hypothesis generation. We'll use data-driven methods to validate our hypotheses and develop a comprehensive resolution plan.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this could be related to a recent product update. Have there been any significant changes to the data catalog feature or related systems in the past two weeks?

Why it matters: Recent changes often correlate with sudden metric shifts. Expected answer: Yes, there was a minor update to the data ingestion pipeline. Impact on approach: If confirmed, we'd focus on the update's impact on data quality checks.

• Considering the nature of data quality checks, I'm curious about the scale of the issue. What percentage increase in failed checks are we seeing compared to the baseline?

Why it matters: The magnitude helps prioritize the response and narrow down potential causes. Expected answer: A 30% increase in failed checks. Impact on approach: A significant increase would suggest a systemic issue rather than an isolated incident.

• Given that data catalogs often interact with various data sources, I'm wondering if this is isolated to specific data types or sources. Are the failed checks concentrated in particular areas of the catalog?

Why it matters: Localized issues point to different root causes than widespread problems. Expected answer: The failures are primarily in structured data from SQL databases. Impact on approach: This would focus our investigation on SQL data processing components.

• Thinking about user behavior, has there been any change in how customers are using the data catalog feature recently?

Why it matters: User behavior changes can sometimes trigger unexpected system responses. Expected answer: No significant changes in user behavior have been observed. Impact on approach: This would shift our focus more towards internal system issues rather than user-driven problems.