Case Study: SimpleRose achieves 90% more data quality monitoring coverage with Bigeye

How the SimpleRose team uses Bigeye to deliver reliable data for large-scale data operations

SimpleRose, a small but data-heavy optimization software company, relied on large-scale data pipelines across S3, Redshift, and Talend to support business-critical operations and a curated data mart. With a lean data team, SimpleRose needed 100% confidence that its data was complete and accurate, but manually writing and maintaining pipeline tests was becoming unsustainable and threatened trust in the company’s single source of truth.

SimpleRose implemented Bigeye’s data observability platform, using its Redshift support, out-of-the-box pipeline and data quality checks, and custom business-logic monitoring. Bigeye was deployed in just a few days and helped reduce time spent on data pipeline monitoring by 50%, increase monitoring coverage by more than 90%, and deliver over $100,000 in annual operational savings.

SimpleRose

Nick Heidke

Principal Data Architect