Case Study: Imperfect Foods breaks down data silos and ensures trusted data with Metaplane

How Imperfect Foods uses Metaplane, Snowflake, and dbt to break down data silos

Imperfect Foods, an e-commerce company focused on reducing food waste, faced the challenge of data silos as it grew. With a small team of four analysts supporting over 200 data consumers, they struggled to centralize their data and ensure its quality, often discovering issues only after end-users reported them in Slack. To address this, they implemented the Metaplane data observability platform to proactively monitor data quality.

The solution involved using Metaplane to automatically monitor their data stack, which included Snowflake and dbt. Metaplane provided alerts with root cause analysis and downstream impact, greatly reducing the time to identify data incidents. The results were significant, leading to a 7-day reduction in time to identify problems and saving the engineering team 4 hours per week, which allowed the entire organization to trust their data.

Imperfect Foods

Adam Smith

Analytics Manager