How a Data Access Policy affect dashboard results

This means:

• The dashboard is always pre-filtered

• These filters cannot be removed

• Users only see the data they are authorized to access

In practice, every dashboard they open shows a filtered version of the data - without the option to expand beyond it.

Example: Single-Value Policy

If a policy is based on one value (for example, Department = Business Development):

• The Department filter is locked

• The user cannot remove or edit it

• Hovering over the filter shows that it’s enforced by a Data Access Policy

As a result, the user can only view data related to that department.

Example: Multi-Value Policy

Some users - such as team leads or regional managers - may need access to multiple segments.

In these cases, a Data Access Policy can include several values, such as multiple recruiter names or locations.

For example:

• Recruiter = Alessandra, David, Hans or James

With this type of policy:

• The user can see data for all assigned recruiters

• The main filter remains locked

• Additional filters can still be applied within this allowed dataset

Filtering Within a Restricted Dataset

When a policy contains multiple values, users can further segment their view.

For example:

• Policy: Recruiter = Alessandra, David, Hans or James

• User adds: Department = Accounting/Finance

Result: The dashboard shows only Accounting/Finance responses where Alessandra, David, Hans or James were the recruiter.

Or:

• User adds: Recruiter = Alessandra

The user can filter by only one of the recruiters in their DAP to further narrow down results.

Summary

Data Access Policies ensure users only see the data relevant to their role:

• Dashboards are automatically filtered

• Policy filters cannot be removed

• Only one policy applies per user

• Multi-value policies allow limited internal filtering

This helps maintain data security while still enabling meaningful analysis.