Case Study: Resident reduces data issues by 90% with Monte Carlo

How Resident Reduced Data Issues by 90% with Monte Carlo

Resident, a house of direct-to-consumer furnishings brands, was struggling with unreliable and hard-to-track data across a very complex stack, including thousands of BigQuery tables and many marketing connections. The data engineering team spent too much time manually checking dashboards, chasing broken or missing data, and dealing with inconsistent logic, weak monitoring, and poor visibility into downstream dependencies.

To solve this, Resident implemented Monte Carlo’s data observability platform, using its real-time monitoring, alerting, and automated lineage capabilities. Monte Carlo helped the team catch anomalies faster, prevent downstream breakages, and collaborate more effectively, resulting in a 90% reduction in data issues and a major restoration of trust in data across the business.

Resident

Daniel Rimon

Head of Data Engineering