---
sourceDocument: Australia Enable AI
sourceDocumentLink: https://www.servicenow.com/docs/r/australia/intelligent-experiences

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    - australia

ft:locale :

    - en-US

ft:publication_title :

    - Australia Enable AI

ft:clusterId :

    - platai

bundleId :

    - platai

workflow :

    - Platform


---

# Data normalization

# Data normalization {#ariaid-title1}

Release version: Australia  
Updated March 12, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read
Summarize  
![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI  
This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.  

## Summary of Data normalization

Data normalization in ServiceNow ensures that extracted data from documents is converted into a consistent, standard format.
This standardization facilitates easier grouping, analysis, and integration of data across fields and with other applications on the ServiceNow AI Platform.
Show full answer Show less  

## Supported Field Types

Normalization supports various field types to maintain data consistency:

* **Date:** Converted to a standard format such as YYYY-MM-DD.
* **Reference field:** Matches extracted values to entries in related tables (e.g., Vendor names matched against the Company table).
* **Integer:** Whole numbers (e.g., 12).
* **Decimal:** Numbers with up to two decimal places (e.g., 12.5).
* **Floating point number:** Numbers with up to seven decimal places (e.g., 12.0000000).

After extraction, normalized values are displayed alongside the original data, with options to edit and correct values if necessary.

## Handling Invalid or Ambiguous Data

If data extracted is invalid for conversion (e.g., letters mistaken for numbers), users must manually correct the values to ensure proper normalization.

For ambiguous data, such as dates that could be interpreted differently depending on format, the default use case configuration determines the interpretation (e.g., "Month first"). Users may need to verify or adjust these interpretations. Automated processing can pause to allow confirmation of ambiguous values, ensuring accuracy before proceeding.  
Data extracted from documents is converted into a standard format. This ensures that values appear consistently across all fields, making the data easier to group, analyze, and integrate with other applications.

Data normalization supports integration with other applications on the ServiceNow AI Platform.{#data-normalization__data-normalization-para-1}

## Field types {#data-normalization__section_ggb_ysl_rxb}

The following field types are converted to support data normalization:
{#data-normalization__table_qbs_lsl_rxb__entry__2}

| Field type | Description |
|-|-|
| Date | Standard date format. For example, YYYY-MM-DD. |
| Reference field | A field that uses a field in another table as a standard. Content Understanding matches the extracted data to the standard. For example, a use case has a reference field called Vendor that points to the Name column in the Company table as the reference. When processing a document task, Content Understanding extracts "Degas Dairy Products, Inc" from the document and fills the Vendor field with that value. Content Understanding compares the value to the company names in the reference table and finds "Degas Dairy Products, Inc" as a match. In the document task, "Degas Dairy Products, Inc" is matched to "Degas Dairy Products, Inc" in the reference. |
| Integer | Whole number. For example, 12. |
| Decimal | Number with up to two decimal places. For example, 12.5 or 12.55. |
| Floating point number | Number with up to seven decimal places. For example, 12.0 to 12.0000000. |
[Table 1. Supported field types for data normalization]

{#data-normalization__table_qbs_lsl_rxb}

## Display {#data-normalization__section_uqy_ctl_rxb}

A completed data extraction field shows the converted value next to it.

You can adjust the converted date value by selecting Edit.  
Note:  
In some cases, the data extracted from the document may not be in a valid format to be converted. For example, if Content Understanding read the letter O instead of a number 0 in a date field (11.12.2o23), then it would not be converted. In this case, edit the field to the correct format.

## Ambiguous data {#data-normalization__section_cxy_r3t_pbc}

When a document contains ambiguous data, Content Understanding uses the default setting in the use case configuration to determine the correct interpretation. The value requires clarification before it can be converted into a normalized format.{#data-normalization__data-normalization-ambiguous-para-1}

For example, consider a use case with a Date field. The default interpretation of ambiguous dates is set to "Month first." A document containing the date 1/2/2024 is interpreted as January 2, 2024, rather than February 1, 2024.

In such cases, you may need to confirm or correct the interpretation of ambiguous dates. Depending on the configuration of the field within the use case, automated document processing may pause so you can verify that the conversion
is accurate.