After the source file has been uploaded, it’s time to map where you want the data to reside in Slate. This is done in four stages. For more information and steps for doing this, select one of the links, below:

1. Field Mappings

2. Prompt Value Mappings

3. Static Mapping

4. Review and Run Import

Use the sidebar on the right to move between the stages.

Field Mappings

Field Mapping dialog window

• Source: Displays the column name (such as Unique ID or First Name) from the incoming file.

• Sample Value: Displays a sample data point from the file from that particular column (such as 653451).

• Destination: First, select the scope of the field. Once chosen, select the specific field from that category to map to. Refer to the Fields and Prompts documentation article for more information on field scopes.

• Destinations 2 and 3: To map an incoming data value to multiple fields in Slate, set destinations in destinations 2 and 3. Repeat the steps from the first destination mapping.

• Overwrite: If some of the persons or data points being brought in already exist in Slate, select this box to prevent the import from overwriting existing data points.

• Unsafe Override: If a field is marked "safe" at the field level, select this box to override this functionality.

• Delimiter: If multiple data values exist in a particular column (such as multiple academic interests or courses), indicate how these values are delimited, and by what delimiter. Once a selection is made, Slate will parse these values out individually, as opposed to storing multiple data values in one field. This will be important at the Prompt Value Mappings stage.

• Group: Groups are critical if bringing in multiple data values relating to a singular, larger item associated with a given person (such as a relationship of that person, a person's school, or a person's job).
  For example, if information about a person's parents was imported, and for each parent, the following data points existed:

  • First Name

  • Last Name

  • Birthdate

  • Email

  • Gender

...then for each set of data points for each parent, the same exact fields would be mapped twice (such as Relationship > First, Relationship > Last, or Relationship > Birthdate).
The difference is that all of Parent 1's data points would be assigned a value of Group 1. All of Parent 2's data points would get assigned to Group 2, and so on. Each group would correspond to one clustering of data points associated with one larger item (such as a parent, a school, or a job). Groups are how Slate associates multiple data points with one larger whole item. 

• Null Handling: By default, Slate only imports information that exists in the source file. However, occasionally a blank or null source value actually indicates valuable data. If you enable custom null handling on a free text field, Slate will set the field value to null if the source value is null. If you enable custom null handling on a prompt-driven field, Slate allows you to map a null source value to the desired destination value in the Prompt Value Mappings stage.
  If Null Handling is enabled on a mapped field, the mapping appears in the Field Mappings Stage regardless of Remap Date. This will be helpful if the column heading of a source file changes from one year to the next. If the remap date is updated, and a previously-mapped column heading does not exist on a new source file, the field mapping will appear with a sample value of "Deprecated (Null Handling)" so that the field can be unmapped.

Field Mapping steps

1. Select a field from the Source Field list

2. Choose Destination from dropdown menus

3. Select Save or the Arrow icon.

   • Clicking Save closes the popup window, whereas clicking the arrow will save the current field's destination value and enable you to customize the next source field’s destination.

4. Repeat these steps for all the Source Fields

Prompt Value Mappings

Only those fields associated with prompt lists in Slate appear at the Prompt Value Mappings stage. Fields that are "text" fields (for example, First Name, Last Name, Email, and Unique ID) will not appear in the Prompt Value Mappings stage since they are all unique and are not associated with a set list of prompts.

1. From the list of Source Fields, select one of the fields

   • Dialog shows incoming Source Values and corresponding Destination Values

2. Select Guess Below.

   • If Slate can match incoming data values with existing prompts, it will do so.

   • This will save time, as opposed to manually assigning each incoming value with a prompt in Slate.

3. Select Save

4. Repeat these steps for the needed Source Fields

   • Unlike Field Mappings, some incoming Prompt Value data points may not need to be represented in Slate. Don't feel compelled to map each and every prompt value if they are not critical.

Once the values are populated, map incoming data values to the appropriate prompt values in Slate. A Mapped and an Unmapped column appear for each field at this stage. The numbers in each column indicate how many incoming values from the source data file have been mapped to prompts values in Slate.

Static Mappings

The Static Mappings stage provides the option to assign a datapoint to every record in an imported Source File that does not exist but should exist. For example, if each incoming record in a data file is a Test Record, but that datapoint does not exist within the imported file, then a "static" value can be assigned to each record in the file so that every record will have a Test Record Tag.

1. If you do not see a Static Mapping matching your desired static value, select New Static Mapping

2. Choose Destination from dropdown menus

3. Select Save

4. Repeat these steps for all desired Static Mappings