Data normalization

  • Release version: Zurich
  • Updated January 27, 2026
  • 2 minutes to read
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    Summary of Data Normalization

    Data normalization in ServiceNow ensures that extracted data from documents is converted into a standard format for consistency across fields. This process enhances data utility by simplifying grouping and analysis, which further supports integration with other applications within the ServiceNow AI Platform. Notably, with the Zurich release, Document Intelligence will be deprecated but remain supported; users are encouraged to utilize the Now Assist in Document Intelligence application for information extraction.

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    Key Features

    • Field Type Conversion: Various field types are standardized for normalization, including:
      • Date: Standardized in YYYY-MM-DD format.
      • Reference Field: Matches extracted data with standard values from another table.
      • Integer: Represents whole numbers.
      • Decimal: Allows two decimal places.
      • Floating Point Number: Supports up to seven decimal places.
    • Display Adjustments: Users can edit converted values directly in the completed data extraction field.
    • Ambiguous Data Handling: DocIntel interprets ambiguous values based on default configurations to ensure accurate normalization.

    Key Outcomes

    By implementing data normalization, ServiceNow customers can expect improved data accuracy and consistency, facilitating better integration with other applications. Users may need to verify and correct any ambiguous data interpretations to maintain data integrity throughout the normalization process.

    Certain types of data extracted from documents are converted into a standard format so that they appear the same across all fields.

    Important:
    Starting with the Zurich release, Document Intelligence is being prepared for future deprecation. It will be hidden and no longer activated on new instances but will continue to be supported. For details, see the Deprecation Process article [KB0867184] in the Now Support Knowledge Base. Instead, you can extract information from documents using the Now Assist in Document Intelligence application. For more information, see Now Assist in Document Intelligence.

    This process increases the usefulness of the data by enabling it to be grouped and analyzed more easily. It also supports integration with other applications on the ServiceNow AI Platform.

    Field types

    The following field types are converted to support data normalization:

    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. DocIntel 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, DocIntel extracts “Degas Dairy Products, Inc” from the document and fills the Vendor field with that value. DocIntel 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.

    Reference field flow.
    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.

    To set the field type, see Create a field for data extraction.

    Display

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

    Data extraction integer field and its converted value field. Data extraction date field and its converted value field.

    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 DocIntel 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

    If there is data in a document that can be understood in more than one way, DocIntel interprets that value based on the default selected for it in the use case configuration. DocIntel must interpret an ambiguous value in order to accurately convert it to the normalized format.

    For example, a use case has a Date field, and Month first is selected as the default order to interpret ambiguous dates. When a document containing the date 1/2/2024 is processed for the use case, DocIntel interprets that date as January 2, not February 1, when it extracts that value and converts it.

    In such cases, the user completing a document task may need to confirm or correct the conversion of ambiguous values. Depending on the field’s configuration in the use case, automated document processing may be interrupted to ensure the conversion is accurate.