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Fixing errors

Last update: September 11, 2026

CREATED FOR:

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  • User

Fix birth day and month

  1. Click on the arrow icon next to the calculated field populating the person.birthDayAndMonth XDM field

    Calculated field expression editor for the birthDayAndMonth fix

  2. Update the expression using the below calculated field code and click Preview

    code language-none 
    concat(date_part("mm", date(birth_Date, "M/d/yyyy")).toString(),"-", date_part("dd", date(birth_Date, "M/d/yyyy")).toString())
    note
    NOTE
    Data should appear as 2 digit month and 2 digit day (i.e. April 27 shown as 04-27). The mm and the dd parameters add 0 padding.

  3. If everything looks good Save the calculated field

  4. Then click Finish to execute the dataflow ingestion.

Validate ingestion

After a few minutes the dataflow run should run and you should see success!

Dataflow run status showing a successful Customer Account ingestion

Monitoring screen

  1. Navigate to the monitoring screen by clicking the left rail on the Monitoring icon under the Data Management section.

  2. Click on the Sources card and then scroll on the bottom bar to see the details for your dataflow run. Note the following:

    • Records received: 20 records were received from the source for processing
    • Records ingested: 20 records were ingested into the Data Lake after the mapping and the data processing.
    • Records failed: You should see a 0 here. This represents the total number of INGEST and DCVS errors. It excludes the MAPPER warnings.
    • Ingestion rate: This is the ratio of records ingested to the records received. 100% of the records received were successfully processed

Sources card on the monitoring screen showing records received, ingested, and failed

NOTE
With partial data ingestion enabled the Ingested Rate for a specific dataflow run can be <100% up to the threshold you’ve set as part of the dataflow details. Also, be aware that 100% success will be reported for dataflow runs where no data was ingested.
NOTE
Note that records cannot be lost.
Records received = Records ingested + Records failed
Ingestion rate = Records ingested / Records received
Partial ingestion threshold = Records failed / Records received

Identities

Click on the Identities card and then scroll on the bottom bar to see the granular details for your dataflow run. Note the following on the Identity Service

  • Records received: 20 records were received by the Identity Store as it was monitoring for new batches i.e. dataset was marked for Profile.
  • Records ingested: 20 records were ingested (i.e. processed for identity information)
  • Records skipped: None as we did not have single identity records or records with now new identity relationships.
  • Success rate (only available in the card): This is the ratio of the records received to records ingested.
  • Identities Added: 40 identities (20 each for CustomerID and 20 for email address) were added to the overall identity graph for the Real-Time Customer Profile
  • Graphs Created: 20 unique graphs were created based on the records it processed (i.e. relationships found in each row of data)
  • Graphs Updated: This would tell you if identities got added to a graph.

Identities card on the monitoring screen showing identity graph metrics

Profiles

Click on the Profiles card and then scroll on the bottom bar to see the details for your dataflow run. Note the following on the Profile Service:

  • Records received: 20 records were received by the Profile store for processing
  • Records failed: None of the records failed. But if they had failed, then you know that it was an ingestion into Profile issue.
  • Profile fragments created: 20 profile fragments were created
  • Profile fragments updated: 20 overall profile fragments were touched
  • Success rate: This is 100%. This is the ratio of records failed to records received.
NOTE
Observe that Records skipped metric is not available for Profile.

Profiles card on the monitoring screen showing profile fragment metrics

NOTE
Note that there is the Destination card and it has metrics that looks similar to what we explored in this lab. These metrics will only make sense once you activate an audience or a dataset.
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