Organizing Datasets with Folders


By default, all datasets are automatically organized into folders based on their object type. For example, if you have multiple Account datasets, they will automatically be grouped under the Accounts folder.


In addition, organizing your datasets into folders can help you keep related data together, streamline cleanup projects, and simplify navigation. The example below walks through the Dedupe panel, but the same process can be used in Cleanse and Transfer as well.

1. Enter Editing Mode

Before creating folders, make sure you’re in Editing mode. Click the pencil icon in the top-right corner of the Dedupe panel.

2. Open the Folders Section

At the bottom of the sidebar, scroll down to the Folders section. To create a new folder, click the ➕ Add button.

3. Create a New Folder

After clicking Add, a dialog box will appear. Enter a descriptive folder name (e.g., Contacts Clean-Up) and click Save.

4. View Your New Folder

Your folder will now appear in the Folders section of the sidebar.

5. Add Datasets to the Folder

Drag and drop datasets from the Datasets list into the folder. Once inside, they’ll be grouped together for easier management.

6. Organize Multiple Folders

You can create additional folders to separate projects, objects, or cleanup tasks.

Best Practices

  • Use clear, action-oriented names (e.g., Donor Accounts Clean-Up, Weekly Review).

  • Group datasets logically (by object type, campaign, or cleanup priority).

  • Review folders regularly to keep your workspace tidy.


Switching Between Dataset Views

In the Dedupe, Cleanse and Transfer modules, you can switch between two dataset views using the toggle in the upper-right corner:


  • Card View (grid icon) – Displays datasets as cards with charts showing duplicates, groups, and total records at a glance. This view is helpful for quickly scanning high-level metrics across datasets.

  • List View (list icon) – Displays datasets in a table format with sortable columns for total records, duplicates, groups, model, and analysis date. This view is ideal when you need to compare details or work through multiple datasets.