Case Study: PagerDuty achieves more reliable data at scale with Monte Carlo

How PagerDuty Applies DevOps Best Practices to Achieve More Reliable Data at Scale

PagerDuty, a digital operations management platform, faced the challenge of managing data quality and reliability at scale across its complex ecosystem of SaaS applications and data pipelines. Their DataDuty team needed to ensure that data met end-user expectations for accurate, timely decision-making amidst the dynamic nature of their business. To tackle data downtime and incident management, they implemented Monte Carlo's data observability platform.

By integrating Monte Carlo, PagerDuty applied DevOps best practices to their data operations. The solution enabled them to monitor data health, intelligently group and route alerts to the correct owners, and suppress noise to focus on root causes. This provided a 360-degree view of their data pipeline health, ensuring critical assets like executive and financial reports remained reliable and that data incidents were resolved quickly and efficiently.

PagerDuty

Manu Raj

Senior Director of Data Platform and Analytics