Collate Product Demo Series - Data Quality + AI Analytics

Your data quality tests aren't the problem. The way they're wired up in your organization is. Most teams run data quality tests, observability tools, and contracts in parallel after writing data to production tables. That leads to bad data in dashboards and AI workloads. More tests don't fix it. Picking the right architecture does.

A global fashion retailer had thousands of data assets across multiple teams, data quality tests the data team owned, and data agreements buried in email threads. None of it worked together, so a failure in one tool didn't surface against the dashboards or AI queries it affected downstream.

After moving to Collate, quality tests, lineage, ownership, and contracts came together in a single semantic graph. Data integration became 3x faster, and data quality enabled higher pricing accuracy which led to increased revenue.

In this demo webinar, you’ll see:

• How to stop bad data before it reaches production tables, with quality tests, lineage, and ownership unified in one graph, and why quality must be a shared responsibility across engineering and business teams
• How AI agents propose and scale test coverage
• A brief overview of Collate AI Analytics, the natural language interface for generating governed dashboards grounded in your actual metric definitions