From Data Validation to Data Value: Building Workflows You Can Trust

Data is moving faster than ever – flowing between systems, powering dashboards, triggering automations, and feeding AI models. But when that data is incomplete, inconsistent, or invalid: automation breaks down, and outputs become unreliable.

Data validation is no longer just a quality control checkbox. It is the foundation for automation, interoperability, and AI success.

In this session, we will cover core data validation principles and modernize them for today’s workflows using both FME Form and Flow. You’ll learn how to build validation directly into your workspaces using geometry validation, attribute checks, coded domains, date and time validation, and JSON and XML schema validation. We’ll then show how to deploy those validation workflows to FME Flow to help reduce manual effort and support enterprise data governance.

We’ll also explore how validation supports GeoAI by improving the quality of training data and helping verify AI-generated outputs.

Join this webinar to learn how to:

• Implement attribute, geometry, and schema validation in FME
• Reduce manual QA through automated validation workflows
• Deploy validation processes in FME Flow to support automation
• Strengthen AI reliability by validating both inputs and outputs

If your goal is trusted automation and higher data value, then this webinar will provide practical, scalable building blocks to get there.