Validate Customer Journey Analytics data with the data validation skill in Coworker

Data quality is the foundation of accurate reporting in Adobe Customer Journey Analytics (CJA). Before building metrics, dashboards, segments, or customer journeys, it is critical to understand whether the underlying Adobe Experience Platform (AEP) data can be trusted.

In this video, you will learn how to use the Data Validation skill in Coworker to quickly assess the quality of datasets powering your Customer Journey Analytics implementation, without writing queries or manually inspecting data.

Discover the datasets behind your CJA reporting

See how Coworker can identify:

By understanding exactly which datasets feed your reports, you can focus validation efforts where they matter most.

Explore dataset schemas and available fields

Learn how to inspect dataset schemas directly from Adobe Experience Platform.

Coworker retrieves schema details and surfaces:

This provides an inventory of fields available for analysis and highlights the difference between fields that exist in a schema and fields that contain usable data.

Validate identity quality

Identity data is essential for Customer Journey Analytics because it supports person-level reporting and cross-channel journey analysis.

In this video, you will see how Coworker:

The example validation shows that ECID and email identities are fully populated and valid in the sample, while the Analytics ID could not be retrieved. This provides a useful signal when deciding which identifiers can support profile stitching and reporting.

Analyze individual field quality

A field may exist in a dataset but still be unsuitable for reporting.

Watch how Coworker validates a campaign tracking field and reports:

In the example, the tracking code values that are present are clean and consistent, but approximately 85% of rows are null. This reveals a major reporting blind spot before a CJA dimension or campaign metric is built on the field.

Perform AI-powered dataset validation

Rather than validating individual fields one at a time, Coworker can evaluate an entire dataset.

You will learn how the Data Validation skill:

The validation results provide a viability map for CJA. Clean fields such as web page name and email code may be ready for reporting, while sparse fields such as purchase value, campaign name, and tracking code require investigation.

Identify revenue and attribution risks

The video also demonstrates how data validation can uncover issues that affect reporting accuracy, including:

In the dataset shown, purchase counts are available, but order amounts are not reliably populated. This is an issue to investigate before trusting revenue reporting.

Why data validation matters for Customer Journey Analytics

Customer Journey Analytics is only as reliable as the data behind it.

Validating datasets before building reports helps teams:

With Coworker, these checks can be initiated using natural language prompts, making data validation more accessible to technical and non-technical users.