Data Transformation Tables

Clicking the Data Transformation Tables tab displays the Data Transformation Tables home page. .
  • In the View section, select an appropriate list view from the drop-down list to go directly to that list page, or click Create New View to define your own custom list view. List views let you display a list of records that match specific criteria.
  • In the Recent section, select an item from the drop-down list on the right to display a brief list of the top records matching that criteria. The choices are listed in the table that follows.
  • From the list, you can click any Data Transformation Table Name to go directly to its detail.
  • Click New to create a new data transformation table.
  • To change the owner of one or more records, select the records you want to change from the list view then click Change Owner.
  • To change the value of an editable field shown in the list double-click it, enter the value you want and click Save.

Recent Choice

  

Description

Recently Viewed

  

The last 10 or 25 records you viewed, with the most recently viewed item listed first. This list is derived from your recent items and includes records owned by you and other users.

Recently Created

  

The last 10 or 25 records you created, with the most recently created item listed first. This list only includes records owned by you.

Recently Modified

  

The last 10 or 25 records you updated, with the most recently updated item listed first. This list only includes records owned by you.

Foundations data transformation tables enable you to transform data received in a message so that it is appropriate for the target field in the subscribing application. You can create data transformation tables that contain one or more data transformations. Each data transformation contains one or two source values and the target value.

When you create a mapping on a messaging relationship, you can optionally specify a data transformation table to apply. For example, you might receive messages via a subscription that contain information about people and their job roles. If a message contains information regarding an architect with one year's experience, you can create a data transformation table to convert this into a specific job role of Junior Architect.

Source Value 1 Source Value 2 Target Value
Architect 1 year Junior Architect

In this second example, a senior business analyst is transformed to a business analyst. The optional source value 2 is not used.

Source Value 1 Source Value 2 Target Value
Senior Business Analyst   Business Analyst
Note:

If a data transformation table contains at least one data transformation that requires two source values, the following rules apply:

  • You must provide an additional transformation source key when creating a mapping.
  • Values sent in the message for the additional key are compared against the values for source value 2. Target data is only transformed if the values sent for the nominated keys match both specified source values.
  • When no source value 2 is specified for a transformation, the corresponding additional key on the message must also be blank to transform the data.

The following table provides examples of mappings that include two sources for data transformation.

Source Value 1 Source Value 2

Matching Value for Source Value 2 on Data Transformation

Data Transformed

 Actual Target Value
Developer 1 year Yes Yes Junior Software Developer
Developer   Yes (null) Yes Software Developer
Senior Business Analyst   Yes (null) Yes Business Analyst
Senior Business Analyst   No No Senior Business Analyst
Notes:

Each data transformation must contain a unique combination of source values.

Source values are not case sensitive.

When creating data transformations for lookups, we recommend that you enter the Salesforce ID of the item to be included instead of text. This reduces the load on Foundations and increases the performance of your integrations.