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

SimpleRose reduces data monitoring time by 50% with Bigeye

SimpleRose, a small firm that develops optimization software, relies on large-scale data operations to power its product. Their challenge was ensuring data correctness and completeness across their high-volume pipelines without dedicating excessive manual effort. The small, agile team, led by Principal Data Architect Nick Heidke, needed to move beyond unscalable manual testing to prevent ETL failures and maintain user trust in their curated data mart.

The solution was implemented using Bigeye, a data observability platform. Bigeye provided automatic data quality checks and pipeline monitoring, which allowed the SimpleRose team to quickly achieve broad coverage of their data warehouse. This resulted in a 50% reduction in time spent writing pipeline tests and over 90% increase in monitoring coverage, saving an estimated $100k in operational costs annually while significantly increasing organizational trust in their data.

SimpleRose