Case Study: Petal improves data quality and speeds up analytics workflows with Datafold

Petal eliminates major data quality incidents with Datafold and saves 15 hours a month

Petal, a fintech company, faced a challenge in ensuring data quality and performance while refactoring its complex dbt models. The company needed to avoid negative feedback loops in its data pipelines and comply with strict regulatory requirements, all while relying on time-consuming manual QA processes for its numerous data changes. To address this, Petal turned to Datafold for its automated data testing solutions.

By implementing Datafold, Petal automated its data quality testing, significantly reducing the time spent on manual QA. The vendor's solution provided critical visibility into data changes and enabled the team to confidently refactor its models. As a result, Petal saved 15 hours of testing per month and has experienced zero major data quality incidents since adopting Datafold.

Petal