Why warehouse native experimentation

Warehouse-native experimentation is an innovative approach that allows companies to conduct statistical analyses directly within their cloud data warehouses, such as Snowflake or Databricks, leveraging existing datasets and compute power to maintain a single source of truth for business metrics. This method contrasts with fully-hosted cloud platforms, which are event-driven and may result in inconsistencies with warehouse data. Warehouse-native experimentation, offered by platforms like Statsig, provides flexibility, agility, and transparency, enabling organizations to re-analyze experiments and adjust metrics dynamically. This approach is particularly beneficial for companies with established data ecosystems, as it facilitates in-depth data analysis and cost-effective experimentation. Jared Bauman from Whatnot highlighted the advantages of this method, such as improved trust, transparency, and flexibility, allowing for meaningful insights and variance reduction in key metrics. Although it requires maintaining storage and compute, the ability to integrate real-time events and conduct comprehensive hypothesis testing makes it a valuable tool for data-driven decision-making.