Data Collaboration Platform (DCP) - Create customized data views

At a glance: Create a customized data view from a data source using transformation and aggregation functions and a data filtering tool. You can preview a sample of the modified data, reflecting the changes as you progress.

Overview

The Data View Builder enables you to create a customized version of your data source and make it relevant to your intended audience without modifying the original dataset. No SQL knowledge, data expert, or data engineer is required, allowing you to perform functions that modify, refine, and summarize the data while previewing it and making adjustments as you go.

Data view functions

The Data View Builder tool includes the following functions:

  • Transformation: Modifying the format, structure, or values of the data to prepare it for analysis. This creates new columns to incorporate insights from existing data.
  • Aggregation: Combining multiple data records to produce summaries or groups using functions such as Sum, Average, Count, Min, and Max.
  • Filtering: Narrowing down the data by applying selected attributes, such as time range, category, or custom parameters to focus on specific relevant information.

The data view builder

The Data View Builder page includes two sections:

Main section

Within this section, you can find the elements for creating your data view, which consist of three key components:

  • Details: Name and describe the data view
  • Customize data: Apply functions and filters for customizing the data
  • Select fields: Select the data view structure by removing unnecessary fields or adjusting the field types.

Side panel

The side panel on the right side of the page presents two sets of data, one from the original source and one from the data view created from it. Each set includes the table name and description, the number of fields, and the number of rows, along with a sample table containing five rows of data. When functions and filters are applied to the data view, the side panel reflects these changes, enabling you to verify the data while modifying it, and compare it with the original version.

Set data view

Learn how to create and customize data views in AppsFlyer to transform, aggregate, and filter data to make it relevant to your intended audience without modifying the original dataset.

Set the data view general details

  1. In AppsFlyer, from the side menu, select Collaborate > Data Clean Room.
  2. From the Sources tab, hover over the source to use for creating the data view, and click  ellipsis.png  (the Menu icon) at the end of the row.
  3. Select Create data view. You’re directed to the New data view page.
  4. Set the following fields:
    • Data view name: Enter a name for the data view. This can be any unique name that will help you identify it. See the naming requirements and guidelines below.
    • Description: Provide a description. This appears as a tooltip when hovering over the data view name under the Sources tab. It's recommended to highlight the primary objectives and data manipulations performed.
    • Adding labels: You can select labels that best represent the structure and data of the view.

Naming requirements and guidelines

  • Make sure the name is unique among all other sources and sub-sources in your account. Otherwise, you won't be able to save it.
  • For cloud integrations, the name doesn't need to match the file name.
  • Name requirements:
    • Length: 2-80 characters
    • Valid characters:
      • letters (A-Z, a-z)
      • numbers (0-9), cannot be the first character of a name;
    • Invalid characters:
      • Spaces
      • Underscore
      • all other symbols or special characters

Customize data 

Apply the tools below to customize your data:

  • Transformation and Aggregation functions: Use to create your custom fields. Each newly created field is added as a column in your data view.
  • Filters: Use selected attributes to narrow down the data and focus on relevant information.

During the process, you can preview a sample of the modified data in the Side panel.

 Note

  • While filters can be added at any point, when Aggregation functions exist, the filters are applied to the data after the execution of these functions.
  • When using functions or filters on date-type fields (columns), ensure that the date values conform to the supported date format specific to the selected function. Any discrepancies may lead to suboptimal choices among the provided options.

Transform and aggregate your data

To create a Transformation or an Aggregation field:

  1. Under Transform & aggregate, click Create field.
  2. Select either the Transformation or the Aggregate tab and click + New field.
  3. Enter a name for the field.
  4. Click Set function, and select a function from the list of functions. The relevant fields for entering the required information are automatically added.
  5. For Aggregation fields:

    [Optional] Click + Condition to apply a condition under which the aggregation occurs before the function is executed.

    Note:

    The condition applies to all Aggregation functions.

  6. [Optional] Click + New field to add more Transformation or Aggregation fields. You can add as many as needed.

Once created, the new field:

  • Appears in the Transform & Aggregate section.
  • Included in the list of fields available for use in all functions and filters.
  • Included in the list of fields under the Select fields section.
  • Reflected in the side panel upon clicking the “Refresh” icon, updating both the sample table and the number of columns or rows.

Filter your data

You can apply filters to narrow down the data by using selected attributes, helping you focus on specific and relevant information.

 Note

When Aggregation functions are added or updated, filters are applied to the data only after they are executed.

To apply filters:

  1. Under Filters, click Filter your data.
  2. Click Select column and select the column to filter.
  3. Click Operator > Equals or Not equals.
  4. Enter a value.
  5. [Optional] Click + Condition to add a condition to the filter.

     Note

    • You can add as many conditions as needed, using the AND and OR operations.
    • Multiple conditions are processed according to the standard logical order, with priority given to AND operations over OR.

List of functions and use case examples

Transformation functions

  • IF: A conditional statement that executes different actions based on whether a specified condition is true or false.
  • CONCAT: The concatenation of two strings
  • CONST: Returns the parameter of the CONCAT function
  • LEAST: Returns the lowest of two values
  • GREATEST: Returns the highest of two values

Example: Transformation

You want to indicate products purchased from the collaborator while masking purchases from other brands you’re working with.

Aggregate functions

  • COUNT: Returns the number of rows.
  • SUM: Returns the sum of all values
  • AVG: Returns the average of all values
  • MIN: Returns the minimum values of two numbers
  • MAX: Returns the Maximum values of two numbers

Example: Aggregation

You want to count the total number of purchases per user and sum the total price they paid.

Example: Aggregation with a condition

You want data on the latest purchases made by users located in Germany and who purchased P&G products.

Select fields

The data view structure includes fields from the original data source and any custom data fields created within the Data View Builder. From here you can remove fields (except for the identifier field) or rearrange their order.

 Note

  • Removing a field when an aggregation function is applied decreases the data granularity, which may impact the results in the data view.
  • When aggregation functions are applied to fields, the fields are removed from the list and placed under the Removed section, without the option to be restored. This happens because the function has combined the data in those fields.
  • After saving the new data view, all the removed fields are no longer visible, and you can’t restore them.
  • Adjusting the field type can only be done from the original source, not from the data view.

Adjust field types

Review each field and select the appropriate data type from the dropdown list beside it to change it.

Remove fields

You can remove any of the fields except for the identifier field.

To remove a field:

  1. Hover over the right side of the field you want to remove.
  2. Click the dustbin icon that appears. The field now appears in the Removed section with a Restore button allowing you to bring back the removed field.
  3. Click Save.

Restoring temporarily removed fields

When a field is removed from the list, it is placed under the Removed section. As long as you haven’t saved the data view, you can restore it by clicking the Restore button.

Save data view

You can save the data view at any stage. Once saved, it is added under the original data source in the list of sources, and you can edit, share, or delete it.

 Note

When saving a new data view, any removed fields are no longer visible. This means you can’t restore them. However, you can always use the original data source to create another data view.

To save the data view:

  • Click Save at the bottom of the page.

Edit data view

The data views appear in the Sources tab, under the original source. You can edit, delete, or share them with collaborators.

Edit general details

You can modify the data view description or the labels used in the description.

  1. In AppsFlyer, from the side menu, select Collaborate > Data Clean Room.
  2. From the Sources tab, click the source name. The associated data views are shown.
  3. Hover over the data view to modify, and click edit-subdomain.png (the Edit icon) at the end of the row.
  4. Edit the description or the labels applied.

Edit customization fields

You can adjust functions and filters, create new fields, edit existing ones, or remove fields from the data view.

To edit the Transformation or Aggregation fields:

  1. Under Customize data, click Manage fields.
  2. Select from the Transform & Aggregate tab the relevant fields to edit and make the necessary changes
  3. Click Save.

To edit the Filters:

  • Under Customize data > Filters, apply the necessary changes to the filter and its conditions.

Note: When Transformation or Aggregation functions are updated, any filters are applied to the data only after their execution.

Edit fields

Delete a data view

When deleting a data view, the following applies:

  • The data view is removed from the list
  • Any associated audiences become expired and appear grayed out
  • Any associated shared data becomes unshared

To delete a data view:

  1. In AppsFlyer, from the side menu, select Collaborate > Data Clean Room.
  2. From the Sources tab, click the source name. The associated data views are shown.
  3. Hover over the data view you want to delete and click  delete.jpeg ( the trash icon) at the end of the row.
  4. From the delete confirmation window, click Delete. The data view is deleted.

Share a data view

Share your data view with the collaborator and provide them permissions.

  1. In AppsFlyer, from the side menu, select Collaborate > Data Clean Room.
  2. From the Sources tab, click the source name. The associated data views are shown.
  3. Hover over the data view you want to share and click share-icon.jpeg  (the Share icon) at the end of the row.
  4. Enter the collaborator's email address, and click Next.
  5. Select the relevant sharing permissions.
  6. Click Save & send.

 Note

You can only share data views created from your sources. Views created from shared sources cannot be shared with others.