How it works

Step 1: Understand the change

Woven captures how data is actually used, not one-off queries.

Woven analyzes downstream query patterns, dashboards, and scheduled workloads to extract durable usage signals — separating exploratory noise from sustained analytical intent.

Step 2: Prepare the data work

Downstream usage becomes review-ready model evolution.

When durable usage patterns emerge, Woven applies data-team playbooks to plan how dbt models should evolve — preparing review-ready changes instead of ad-hoc fixes or one-off views.

Step 3: Decide and propagate data work

Analytics engineers decide how and when the prepared data work propagates downstream.

Woven batches review-ready analytics engineering work into a prioritized digest, so analytics engineers decide what to apply, defer, or ignore – letting data teams scale from a few stakeholders to dozens without alerts or coordination drag.

Team outcomes

As the business evolves, analytics keeps up – without creating a bottleneck.

Woven turns continuous business change into a steady, reviewable stream of analytics improvements – so teams move faster without shifting ownership, adding coordination overhead, or burning out the data team.

Get started

Start with observation, then expand where it helps.

Begin with a two-week Snowflake Usage Insights pilot that surfaces evolving query patterns without taking action. Selectively enable preparation and review workflows to validate impact before expanding further.