When DQE processes a file, the output file usually contains the original input data plus additional DQE columns.
These columns help you understand which services were applied, which values were validated or standardized, and which records may require review.
Who should read this article
Use this article if you have received a processed batch file and need to understand the DQE columns added to your data.
This article applies to file-based batch processing via SFTP. It does not apply to real-time API responses.
Why DQE adds output columns
DQE output columns are added to make the processing results easier to review and use. Depending on the services enabled for your project, DQE may add columns for validation status, standardized values, error codes, enrichment data, or matching information.
The output columns are designed to help you answer questions such as:
- Was the record processed successfully?
- Was the input value valid?
- Was the value corrected or standardized?
- Which quality or error code was returned?
- Was additional enrichment data found?
- Should the record be accepted, corrected, reviewed, or rejected?
Output columns depend on your processing configuration
The exact list of DQE columns depends on the services included in your batch processing configuration.
For example, a file processed with postal address validation will not return the same DQE columns as a file processed with phone validation, email validation, identity checks, geolocation, or company data enrichment.
You may only see the columns related to the services enabled for your project.
Main families of DQE output columns
DQE output columns are usually grouped by processing service.
| Processing service | Purpose | Detailed article |
|---|---|---|
| Postal address validation (RNVP) | Standardizes postal address fields and returns postal quality indicators. | Postal address output columns (RNVP) |
| Phone validation | Checks and reformats phone numbers. | Phone output columns |
| Phone Plus validation | Adds extended phone information such as operator and portability data. | Phone Plus output columns |
| Email validation | Checks email quality and returns validation information. | Email output columns |
| Email activity | Returns activity information when the E-Activity service is enabled. | E-Activity output columns |
| Gender and name checks | Checks first names and last names, detects possible inversion, and returns gender information or suggestions. | Gender output columns |
| ID Check | Checks whether an individual or household can be identified at the provided address. | ID Check output columns |
| ID Check Plus | Extends ID Check with additional comparison fields such as birthdate, phone, or email. | ID Check Plus output columns |
| Geolocation | Returns geographic coordinates and geolocation status. | Geolocation output columns |
| B2B enrichment | Returns company matching, status, and certainty information. | B2B output columns |
| B2B Notice 80 | Returns company matching or enrichment information for B2B Notice 80 processing. | B2B Notice 80 output columns |
How to read dynamic field names
Some output columns include a placeholder such as {FIELD_NAME_PHONE}, {FIELD_NAME_EMAIL}, {FIELD_NAME_FIRSTNAME}, or {FIELD_NAME_LASTNAME}.
This placeholder is replaced by the name of the input field processed by DQE.
For example, if your input phone column is named mobile, a column such as DQE_{FIELD_NAME_PHONE}_ERROR_CODE may become DQE_mobile_ERROR_CODE.
How to use DQE output columns
DQE output columns should be used to review and decide what to do with each processed record.
| Type of information | How to use it |
|---|---|
| Standardized or reformatted value | Use it to update your data when your business rules allow automatic correction. |
| Error code | Use it to identify invalid, incomplete, uncertain, or unprocessed values. |
| Error label | Use it to make the result easier to understand for business users or support teams. |
| Quality code | Use it to evaluate the level of confidence or quality returned by the processing service. |
| Enrichment data | Use it to complete or qualify your original record. |
Best practices
- Keep the original input columns to compare source data and DQE results.
- Use the record identifier to match each output row with the original input record.
- Review error codes before applying automatic updates to production data.
- Only use output columns that correspond to services enabled for your project.
- Document your internal business rules for accepting, correcting, reviewing, or rejecting records.