Manage Columns

Table data is organized in columns. Multiple columns with the same denominators can be visually represented to users as a group.

Add Columns

Quote table columns can either pull data via Iron Python scripts (the system supports a maximum of 3000 rows) or users import data manually.

  1. In Define Columns, click Add Column.
  2. Define the column label and the column name will automatically populate.
    Make sure not to use the reserved SQL words for the label.
  3. Choose the column type.
    The column type conditions the input users can make in the field.
  4. Click Set Permissions.
    The Permissions pop-up displays.
  5. Select the permission groups for which the column will be editable/hidden.
    Please note that columns in quote tables are in read-only status by default.
  6. (Optional) Set the validation rules.
    Read more about the quote table validation rules in the Validation Rules section.
  7. (Optional) Select the Contains Personally Identifiable Information checkbox.
    All information that could potentially be used to identify an individual should be flagged as PII.
  8. (Optional) Select the Sensitive Data checkbox.
    PII data can be flagged as sensitive for an additional layer of protection.
  9. Save your changes.
  10. Repeat the previous steps to add more columns.
  11. Click Save.


Validation Rules

Validation rules show users that data input is required and that a limit on the data users enter is required.
The table shows which validation rules are available for which column types:

Column TypesValidation Rules
NumberMinimum
Maximum
Decimal
Money
StringRequired
Contains
DateRequired
Attribute
BooleanNot available

To manage validation rules for a column, access Validations when editing the column. After selecting a validation rule and entering the value, you should define the validation message that will display to users if they break the rule. The validation message is required, otherwise you cannot save the columns.
If one or multiple validation rules are broken, the validation of the entire table fails and an informational message displays above the table. However, users will still be able to save the quote table and the quote and manage other sections independently.
A rule's activity is handled through the Active checkbox. If a rule becomes temporarily unneeded, but may become required in the future, unselect the checkbox.

A flag on quote tables, with values True or False, denotes the validation status. As the flag is exposed for scripting, scripts can be created for users to perform actions based on the validation status.

Scripting

Quote table columns are exposed for scripting so that you can create scripts for complex calculation of the tabular data. Additionally, you can manage validation rules and column labels via scripts:

  • Validation Rules - the new method ExecuteValidations triggers the validation rules. To check if a validation rule is broken, use the HasValidationsFailed method. Also, you can activate/deactivate a rule via scripting (e.g. add a pre/post action to actions and cell events in the Actions tab). Changing the activity via scripting does not influence the behavior of the Active checkbox in Setup (the activity must be changed manually).
  • Column Labels - column labels are exposed for scripting so you can rename them to meet specific business needs of your users. You can create a global script that renames the labels after users execute the event to which the script is attached in Events.

Example

A script affecting the Payment Schedule quote table is attached to the action of changing the date of the first payment in the designated quote custom field. After users change the date, the script is triggered and the First Installment label is replaced with October 15.
Please note that in the example quote, the parameters Quote.QuoteTables and GetColumnByName retrieve quote and column names, respectively, not labels.
Quote.QuoteTables[“Payment_Schedule”].GetColumnByName(“First_Installment”).Label = “October 15”

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