Purpose

This article explains dashboard-level filters — the controls that sit alongside the Date range pill at the top of every tab, letting viewers re-slice data across every widget whose dataset exposes the matching column, without opening the builder.

Audience

• BI Analyst — decides which filters to expose and understands how they map to dataset columns.
  
• Admin — needs authoring access to configure filters.
  
• TA Leader — primary consumer, uses filters to re-slice by business unit, department, recruiter, etc.

The following video provides a quick walkthrough of this process.

Configuration

1. Open the filter editor

1. In the Dashboard Builder, locate the Edit Filters pill at the top right of each tab, next to the Date range pill — the number on it shows how many filters are currently configured (this count includes filters that aren't pinned as visible pills).
   
2. Click Edit Filters. The All filters panel opens from the right side of the screen, showing every filter currently configured on the dashboard, grouped by category.

2. Add filters

1. From the All filters panel, click + Add filters. The Add filters dialog opens.
   
2. Choose between two tabs: Default filters (product-shipped filters — date range, source category, business unit, department, location country, recruiter, hiring band, etc.) or Custom field filters (built on tenant-specific custom fields — only visible if your tenant has configured any).
   
3. Filter categories are generated dynamically based on the base tables and columns available in the datasets powering the dashboard's widgets — they are not a fixed list. A dashboard built on different datasets will surface different filter categories entirely. A category (and its filters) only appears if it's actually being used in a widget on that dashboard. For example, on a dashboard built on the Applications and Positions datasets, the Default filters tab groups filters as:
   
   • Global — Filters that apply across all datasets, like Date range. Date range is permanently pinned and cannot be removed — every dashboard surfaces it.
     
   • Dataset — Filters defined on dataset-level columns.
     
   • Applications — Filters defined on columns of application records, such as Source Type, Referred. Used when the dashboard is about candidate flow.
     
   • Positions — Filters defined on columns of position records, such as Business Unit, Department, Location Country, Recruiter, Hiring Band. Used when the dashboard is about pipeline or hiring outcomes.
     
4. Check the box next to each filter you want to expose.

3. Understand how selection behaves

1. Checking a filter adds it to the All filters panel and also surfaces it as a pill at the top of the tab area, giving viewers one-click access.
   
2. Pills can show up to 4 filters at once — beyond that, the rest remain accessible via Edit Filters rather than as pills.
   
3. Unchecking a filter removes it from both the All filters panel and the top pills.

4. Choose filters deliberately

1. Pick filters that match how the audience will actually want to re-slice the data — e.g. Department, Location Country, and Recruiter for a recruiter-facing dashboard; Business Unit and Hiring Band for a leadership view.
   
2. Avoid filters that are mostly null across the data — a filter that's empty for 80% of records produces confusing results and erodes trust.

5. Know which widgets a filter actually affects

1. Every filter maps to a column on one or more underlying datasets — the category shown (Global/Dataset/Applications/Positions) reflects which datasets expose that column (e.g. Business Unit lives on position records, so it's under Positions).
   
2. When a filter is set, the platform checks each widget's underlying dataset: if the dataset exposes that column, the widget respects the filter and recomputes; if not, the widget ignores it and its value doesn't change.
   
3. Each widget shows a "filters applied" indicator at the bottom stating how many dashboard filters it actually responded to — click the info (i) icon to see the list.