Direct answer: A safe Power BI workspace cleanup starts by defining scope, inventorying every item, retaining usage history, identifying owners and dependencies, and classifying candidates. Notify owners, archive recoverable definitions, approve the change, and only then remove content. Never turn a low-view filter directly into deletion.
Workspace cleanup is not housekeeping alone. Removing the wrong semantic model can break multiple reports; retaining every abandoned item leaves users unsure which content to trust. The goal is a smaller, understandable active estate with a decision record.
Before the cleanup
- Define the scope: tenant, domain, capacity, workspace group, or one workspace.
- Choose the evidence window: match it to expected report frequency. A monthly operational report and an annual compliance report need different thresholds.
- Freeze the decision rules: document what creates a candidate and which exceptions require manual review.
- Name accountable reviewers: assign a technical steward and business owner; do not route every decision to the administrator.
Build the cleanup inventory
Capture stable workspace and item IDs, names, types, owners or contacts, endorsement, sensitivity label, created or modified date, refresh state, downstream connections, and status. Microsoft’s scanner APIs can extract tenant metadata including item names, owners, labels, tags, and endorsement state.
Join that inventory to views, viewers, last activity, and trend. Items with no usage events still need to appear; starting from activity alone can omit the quiet content you most need to review.
Classify each candidate
| Outcome | Use when | Next action |
|---|---|---|
| Keep | Active, critical, or legitimately infrequent | Record reason and next review date |
| Promote | Useful but hard to discover | Improve catalog, training, placement, or communication |
| Improve | Demand exists but performance or usability is weak | Assign an owner and measurable change |
| Consolidate | Another item covers the same need | Validate dependencies and redirect users |
| Archive | No present need but recovery is required | Store the definition and decision record securely |
| Retire | Owner-approved, replaced, dependency-checked content | Delete in the approved change window |
Required safety checks
- Confirm the official owner and business purpose.
- Check lineage and impact analysis for dependent reports and models.
- Ask whether use is seasonal, regulatory, executive, or meeting-driven.
- Identify the certified replacement and communicate its link.
- Archive the latest definition, configuration, and documentation.
- Test the recovery path rather than assuming the archive works.
- Record approval, date, operator, reason, and affected IDs.
Microsoft’s lifecycle guidance specifically recommends selecting scope and time window, reviewing usage and metadata, communicating with owners, verifying dependencies, choosing an archive, and confirming recovery before deletion.
Use a queue, not a purge list
Name the output “Workspace cleanup review.” Show why an item was flagged, the evidence period, its owner, exception, proposed action, and approval status.
Frequently asked questions
How often should Power BI workspaces be cleaned up?
Microsoft notes that organizations may run cleanup monthly or quarterly depending on content volume. Choose a cadence that matches your estate and assign it to an operating owner.
How many days of no usage makes a report unused?
There is no universal threshold. Microsoft gives examples ranging from one month for daily operational content to three months for quarterly content. Define thresholds by expected frequency and document exceptions.
Should unused reports be deleted immediately?
No. Validate ownership, expected use, replacement, dependencies, archive, recovery, communication, and approval first.
Primary sources
- Microsoft Learn: Retire and archive Power BI content
- Microsoft Learn: Metadata scanning overview
- Microsoft Learn: Data source impact analysis
Turn cleanup into a repeatable operating process
UsageVault brings usage, inactivity, audience, performance, and portfolio context into one Power BI model while your team retains the data in SQL.