The complete inventory of your tenant: aggregate counts of every Power BI and Fabric item type (semantic models, reports, dataflows, lakehouses, notebooks and the rest), plus a detailed per-workspace table with owners, users, creation date, last activity, capacity assignment and item counts.

Trace every dependency from data source to report, across workspaces. Search the lineage view for a specific table or column.

Workspace and per-item access, with AD / Entra groups expanded so you actually see who has access to what.

Potential savings (% of each model that is unused), refresh history, schedule, average duration, tunable best-practice rules (choose which to run, adjust thresholds), KPI inventory and storage mode for every model.

Inventory every Dataflow with its M code, refresh stats and number of queries. Search across all M expressions.

All Apps — including organizational Apps — with audiences, 28-day consumption, contacts and access details.

Report opens and page views with page-level granularity, average load time per user and report subscriptions — rolled up per report. Consumption data accumulates beyond the 28-day limit so you build months or years of usage history automatically.

See immediately — tenant-wide — where a report renders an error instead of data. Every report shows how many visuals are broken, drillable per page and per visual; right-click to see exactly which visuals broke and why (missing fields, broken DAX, broken visual-level filters). Combine with page views to fix high-traffic reports first. Coming in v2.9.5.

Break down Capacity Unit (CU) consumption per capacity, workspace, item type, item and operation — with 24h and 7-day trend arrows so you can see what's driving the bill and what's spiking. History accumulates past Microsoft's 14-day window, so you keep a continuous record of what each item costs.

Every custom (non-Microsoft) visual across the tenant — which reports use it, how many users consume it, and whether it's certified. Spot uncertified visuals that could send data externally, and compare per-user counts against your license entitlements to find compliance gaps or overspend.

Average run time, lines of code and language per notebook. Search every notebook for packages or keywords.

Every slice of the scan — DAX, M, lineage, refresh events, RLS, best-practice violations, page-level consumption with load times, and capacity metrics — as raw JSON files. Feed them to a frontier AI for prioritized optimization suggestions, or pipe them into a governance dashboard.

Local files or downloaded from the Service. Visual-level usage, page-level filters, report-level measures and mobile-layout artifacts.

Online thin / live reports analyzed without downloading — composite and child models, personal, Pro and Fabric workspaces. Full downstream lineage from a shared model to every report, Excel pivot and chained model.

Same usage detection for paginated reports — parameters, embedded datasets and data sources included.

Find which columns power your Excel users — pivot tables are detected by querying activity logs.

Across every report and model — with statuses like "Used in visuals", "Used in measures" or "Unused".

Usage tracked for the modern model objects too — calculation groups, KPIs and field parameters.

Spot implicit measures, unused calc groups, RLS gaps and other DAX best-practice violations.

For any column or measure, see every downstream visual and upstream source — your impact analysis, done.

Cleaned TMDL of your model with unused objects removed — the fastest and safest way to optimize a semantic model nowadays. Apply via the TMDL view in Power BI Desktop or the Service.

Every DAX artifact removed via Kill DAX is auto-backed up and restorable with one click. Teams can share the backup folder via OneDrive.

.measurekiller files save the full scan results. Exit today, pick up tomorrow — or hand the analysis to a colleague who runs the cleanup under their own permissions.

Remove unused objects from semantic models directly — no round-trip through Power BI Desktop. Revertible via Power BI's built-in version history.

Step-by-step suggestions to clean and optimize your model — remove unused artifacts, disable auto-datetime, find unused pages or visuals that might consume unnecessary columns of your model — each with an estimated size reduction so the biggest wins surface first.