Case Study: Clearcover increases data quality coverage by 70% with Monte Carlo

How Clearcover Increased Quality Coverage for ELT by 70 Percent

Clearcover, a Chicago-based tech-driven insurance carrier, needed a way to maintain trust in its data as it scaled a modern ELT stack with Snowflake, Fivetran, dbt, and Prefect. As the number of data sources grew, the team found it increasingly difficult to scale data quality testing manually across pipelines, leading to reliability concerns and bottlenecks in delivering prepared data to the business.

Clearcover turned to Monte Carlo for end-to-end data observability, using automated monitoring, alerting, lineage, and monitors as code to improve coverage and reduce noise. With Monte Carlo, the team increased quality coverage for raw data assets by 70%, cut time to resolution for incidents by 50%, and was able to catch issues earlier, reduce data incidents, and have more proactive conversations before problems affected stakeholders.

Clearcover

Braun Reyes

Senior Manager of Data Engineering