Introduction to the SundaySky Data Library

Once you have decided that you want to personalize your video, you're ready to set up the data library.

The library is a collection of data fields that are used to personalize content. It helps to think of a data field as the entity by which you want to create personalization. The data field that you create in the SundaySky data library correlates directly to the data field that exists in your source data record. When the video is published, the value that is in each record is passed to SundaySky and displayed according to viewer.
A data field can hold a value that is unique to each viewer such as first name, or it can represent an attribute—for example, department—that can hold several values (Finance, HR, Product).

SundaySky supports two methods of personalization: Personalization Token and Audience Messaging. Following are descriptions of each method.

Personalization Token
How is it used? Data from the source data record is inserted into the video.
Example Hi {first name}!
How do I implement this in the data library? In most cases, you'll only need to create a data field that represents the content that you want to personalize.

For the example above, you would create a data field called First Name.
Special guidelines If you are implementing this method in a voice-over narration, you'll need to create a data field and the possible values that the data field can have.

For example:
If you are narrating Thank you for purchasing the {plan name} plan
you would create a data field called Plan Name together with the possible values that this data field can have (gold, silver, platinum). 
Audience Messaging
How is it used? The video is personalized for a specific audience segment according to a value that exists in the source data record.
Example The on-screen text displayed to the viewer is based on the age group to which the viewer belongs.
How do I implement this in the data library? You'll need to create a data field that represents the audience attribute, together with the values that this attribute can have.

For the example above, you would create a data field called Age Group together with the possible values that this data field can have (Gen Y, Gen Z, Baby Boomer).


The data fields that you create need to represent actual data that exists in your company records. If you create a data field for a viewer's first name, the first name of your viewer needs to exist in the data source that you will connect to your video.

If you are creating a video using a Story Template, the library is populated automatically with the data fields relevant to the personalization that you chose. Nevertheless, you can add, edit, and remove data fields as required.


Data Library Structure

Each video in your account has a unique data library that is used for managing the data fields. Each row in the library represents a data field that is used somewhere in the video to create personalization. If the video you are creating does not include personalization, this library will be empty.

data_library_structure.png

Following are descriptions of the library columns and components:

a Data field This is the name given to the data field so that it can be identified easily.
b Source field This column is populated with the source field name after a data connector has been selected for the video.
c PII If the data field has been designated as a field that contains personally identifiable information, it will be indicated here.
d Used in This column displays the scenes in which the data field is being used. If the data field is being used for Brand by Audience, this will also be indicated here.
e Values When a data field has been defined to include values, they are displayed here. Hovering over the content displays the full list of values.
f Search bar If your video includes many data fields, using the search bar locates the desired data field quickly.
g Data connector Clicking Select enables you to choose the data connector for the video.


This article provides guidelines for adding and editing data fields and values manually. If you prefer, you can also build your library by uploading a file with the relevant data fields and values. See Creating Data Fields in Bulk to learn how.


Adding a Data Field

1.

Open the relevant video in the Studio.

2.

Select Data in the sidebar.

 

Select_data_sidebar.png

 

The data library is opened. Note that one data field, id, already appears. This is a system data field that is used for reporting purposes. It cannot be deleted.

 

Data_library_new.png

3.

Click Create Data Field.

 

Click_create_data_field.png

4.

Enter the name of the data field.
▶ Data field names are case-sensitive. For example: entering Department Name followed by department name creates two different data fields.

 

Enter_field_name.gif

5.

(Optional) If you have already selected a data connector, enter the Source field to make sure that it matches the field that you are sending to SundaySky. The names needs to match exactly.
▶ This field is activated after you select a data connector.
▶ When this field is activated, clicking in the field enters the source field automatically (according to what you entered for the data field name). It can then be edited as required.

 
After Selecting Data Connector Before Selecting Data Connector
Populated_field.gif
6.

(Optional) Check the PII checkbox if the data field pertains to personally identifiable information (e.g. social security number, home address).
▶ If you mark this checkbox, the data field will not be included in any analytics and data related to this field will never be stored.*
* If you are using the File Upload data connector, data related to this field will be stored on a highly secure SundaySky server.

 

PII.png

7.a.

(Optional) If this data field is being used for audience messaging or in a voice-over narration, add all the values that this data field can have.
To begin, toggle the switch to Use value list.

 

Use_value_list.png

7.b.

Enter the name of the first value and then click + (or ENTER) to enter all the values that you need.
▶ Value names are case-sensitive. For example: entering Product followed by product creates two different values. Make sure that the value exactly matches the value in your company records.
▶ If the data field requires many values, you may prefer adding them using a spreadsheet. See Creating Data Fields in Bulk to learn how.

 

Create_values.gif

 

Note:
There is a maximum limit of 2,000 values per data field. If there are more than 2,000, only the first 2,000 will be created.
In regards to narration: a value that is not included in the value list will trigger the default narration sentence.

7.c.

After you have added all the values, click Save.

  The data field is added to the library.

 

Data_field_is_added.png

8.

Click Done to close the library.

 

You are now ready to personalize your video with the data field you created.
In the example below, the data field Department Name (as defined in the data library with the relevant values) is ready to be used for audience messaging.

 

New_data_field_ready.png


Editing a Data Field

1.

Hover over the data field that you want to edit.

 

Hover_over_data_field.png

2.

Click the three-dot menu at the end of the row and then select Edit.

 

Click_edit_on_row.png

3.

Edit any component of the data field as required (Name, Source field, PII).
▶ In the example below, the name has been edited.

 

Data_field_name_edited.png

4.

Click Save.

 

The data field name is updated.

 

Data_field_name_updated.png

5.

Click Done to close the library.


Editing a Data Field Value

If a data field in your library includes values, you may need to edit them or add new ones.

1.

Hover over the data field whose values you want to edit.

 

Hover_over_values.png

2.

Click the three-dot menu at the end of the row and then select Edit.

 

Click_edit_on_row_values.png

3.

Edit the values using the following options:
(a) Click + to add a new value;
(b) Click the pencil icon to edit an existing value.
▶ If you need to make a lot of changes to the values, you may prefer adding a new list using a spreadsheet. See Creating Data Fields in Bulk to learn how.

 

Edit_values_new.png

4.

After you have finished making all the changes, click Save.

 

Save_edited_values.png

 

The data library is updated accordingly.

 

Values_updated.png

5. Click Done to close the library.

Deleting a Data Field

A data field can only be deleted if it is not being used in a scene or for Brand by Audience. If it is, you'll need to remove the data field from the scene or Brand by Audience before you can delete it from the data library. For your convenience, you can refer to the data library to see where the specific data field is being used.

Delete_used_in.png

1.

Hover over the data field that you want to delete.

 

Hover_over_row_delete_field.png

2.

Click the three-dot menu at the end of the row and then select Delete.

 

Delete_data_field.png

3.

If you are sure that you no longer need the data field, click Delete.

 

click_delete_confirmation.png

 

The data field is deleted.

 

data_library_after_deletion.png


Deleting a Data Field Value

You can delete a value that you are no longer using. Note that there are two cases in which, when deleting values, you will need to keep at least one of them:
a) the data field is being used for audience messaging
b) when using a personalization token and the data field is being used for narration

Just like for data fields, you can refer to the data library to see which values are being used in scenes and for Brand by Audience.

values_in_use.png

1.

Hover over the data field that is associated with the value that you want to delete.

 

Hover_value_delete.png

2.

Click the three-dot menu at the end of the row and then select Edit.

 

select_edit_delete_value.png

3.

 Click the trash icon by the value that you want to delete.

 

delete_value_trash_icon.png

4.

Click Save.

 

save_delete_value.png

 

The data library is updated accordingly.

 

data_library_updated_after_value_delete.png

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