A Data Type is a type of metadata that is used by the system to specify the kinds of characters expected, or not expected, for a field. Choosing the most appropriate data type ensures high automation rates and fewer Supervision tasks. You can learn more about data types in the What is a Data Type? article.
Overview of options
The table below gives a high-level overview of the options that suit specific kinds of content. More information about each option can be found in the sections below.
If your field contains... | ...choose this option: |
|---|---|
Standard content that’s not specific to your organization (e.g., addresses, currencies, dates) | One of our default data types, if one matches your field’s content |
Data in a specific pattern (e.g., -) | A pattern custom field data type |
A list of specific valid values (e.g., provider names, authorization codes) | A list of custom field data type |
Data that has an existing ML configuration, but you’d like the data type’s name to match the name of the field | A data type with an existing ML configuration |
Default data types
Hyperscience offers a wide variety of predefined data types that you can use for your fields. Always pick the most appropriate data type, considering the characters you expect the field to have. We recommend checking the Supported Characters and Default Data Types article to learn more about the predefined data types. Understand what characters each data type supports in Default Data Types.
Custom data types
Though a default set of data types is included in each Hyperscience instance, you can also create new data types in three different ways:
With a custom pattern
Based on a list of expected values
With existing ML configurations
Pattern custom field data types
Pattern custom field data types validate field values against a set pattern. We recommend using this data type in cases where a pattern is expected – for example, an account number of two letters, followed by seven digits, with a particular separator value such as a dash or slash. To learn more about pattern custom field data types, see Creating Data Types with Custom Patterns.
List custom field data types
List custom field data types to validate field values against a user-provided list of values. You can use this data type in cases where only certain field values are allowed and expected (e.g., a list of account types, or a list of states or provinces). To learn more about the list of custom field data types, see Creating Data Types with a List of Expected Values.
Data types with an existing ML configuration
Data type with an existing ML configuration allows you to use a descriptive display name that matches the field’s name, while also using an existing ML configuration. You can use this data type in cases where there is already an existing ML configuration for your field but you’d like to use the configuration under a different name. For example, creating a new data type named "Account Number" using the ML Configuration value "Entry Alphanumeric" is recommended because it allows users of the Layout Editor and Field Dictionary to more easily recognize and select the correct data type. To learn more about data types with existing ML configurations, see Creating Data Types with ML Configurations.