Data mapping & quality
After connecting a source, map its fields and review data-quality checks so downstream analysis can be trusted.
Data mapping & quality
Connecting a source is step one. Before its data can be trusted in audits, you map its fields to the platform''s model and review the quality checks.
Mapping
When a source carries custom fields — CRM stages, revenue columns, product attributes — you tell the platform what each one means. Mapping lives in the connection flow under Settings → Data Sources. Good mapping is what lets the platform talk about "qualified leads" or "revenue" in your terms.
Quality checks
The platform runs data-quality checks on incoming data and flags problems such as missing values, unexpected gaps, or fields that do not line up with what an audit needs. Resolving these before running an audit keeps findings reliable.
Why it matters
Measurement quality is the trust chain for everything downstream. If conversions are mistracked or revenue is unmapped, recommendations built on them will be wrong. The Measurement Health section turns these checks into an explicit score and a set of findings.