Warehouse Native Experimentation Results
Overview
Understanding how your experiment is performing, and whether it's driving meaningful impact, is key to making confident, data-informed product decisions. Warehouse Native experiment results help you interpret metrics derived directly from your data warehouseA centralized repository for storing and managing large volumes of structured and semi-structured data. Examples include Snowflake, BigQuery, Redshift, and Databricks., assess experiment health, and share validated outcomes with stakeholders.
View experiment results
Review key experiment metrics and overall significance in Harness FME.
Explore how each metric performs across treatments, inspect query-based data directly from your warehouse, and understand how results are calculated based on your metric definitions.
Analyze experiment results
Drill down into experiment details to validate setup, confirm metric source alignment, and investigate user or account-level behavior.
Use detailed metric breakdowns to identify anomalies or confirm expected outcomes.
Share results
Download experiment metrics, statistical summaries, and warehouse query outputs in CSV or JSON format for further analysis or collaboration with your team.
You can also share experiment results directly within Harness FME to maintain visibility across product, data, and engineering teams.