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On-demand Webinar
On-demand Webinar

Experiment at AI Speed: Warehouse Native Analytics for Modern Teams | Harness Resource | Harness

Run Faster, More Reliable ExperimentsAI is accelerating release cycles, but most teams can’t trust their experiment results.Data is duplicated. Metrics don’t match. And decisions get delayed while teams debate what’s accurate.There’s a better way.Join Harness in this live webinar to see how modern teams run experiments directly on their data warehouse using governed, real-time data and shared metrics.In this session, you’ll learn how to:Eliminate conflicting metrics and finally trust your experiment resultsRun experiments faster—without rebuilding or duplicating pipelinesAnalyze results directly in your warehouse using real-time, governed dataScale testing across teams without adding complexityThis session is for you if you:Don’t fully trust your experiment resultsAre duplicating data across tools or pipelinesWant faster, more reliable insights from testingDon’t miss how leading teams are fixing experiment accuracy and speed.

Published
March 24, 2026

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What you'll learn

Key Takeaways

AI Coding Tools Increase Downstream Complexity

While AI accelerates code generation, it creates more features to test, secure, and deploy. Teams often spend the majority of their time managing this increased volume rather than building.

Change Is the Atomic Unit of Risk

Every system outage, security breach, and incident originates from a change. Modern product development must prioritize shipping new features safely and securely at scale.

Centralize Analytics With Data Warehouse Native

Using a data warehouse as the single source of truth prevents divergent metric definitions and eliminates the need to duplicate data pipelines. This approach keeps sensitive data secure while accelerating testing.

Protect Performance Using Guardrail Metrics

Guardrail metrics monitor system health during an experiment to ensure new features do not degrade performance. For example, tracking response latency ensures an AI assistant remains fast while measuring its resolution rate.

Flexible Integration With Existing Feature Flags

Data warehouse native analytics can join assignment sources with metric sources using unique user IDs. This flexibility allows organizations to run experiments even if they use different vendors for feature flagging.