Power BI Usage Analytics: What Native Metrics Miss and How to Fill the Gap

Power BI's usage metrics report is built into every workspace. Open a report, click the ellipsis, select 'View usage metrics report,' and you get a pre-built dashboard: total views, unique viewers, views by day, and a breakdown by report page. It works, and for a newly published report in its first month, it tells you exactly what you need to know.

The problem is the retention window. Power BI retains activity data for 30 days. Not 30 days of rolling history — 30 days total. A view that happened 31 days ago is gone. It cannot be retrieved, re-queried, or exported retroactively.

This creates three practical problems that BI teams hit regularly.

Licence justification requires history. When finance asks whether 40 Power BI Pro licences are justified, the right answer is based on who actually used which reports over the past year — not who opened something in the last month. December usage patterns look nothing like August usage patterns. Annual licence reviews require annual usage data. 30 days cannot support that analysis.

Report retirement requires evidence. Retiring a report that nobody uses sounds easy. In practice, someone always says 'that report is used for the end-of-quarter process — it won't show views most months.' With 30 days of data, you cannot prove or disprove this. With 12 months of data, you can show exactly when the last view occurred and whether the quarterly usage pattern is real.

Trend analysis requires more than a month. A report that shows 200 views last month might be growing, declining, or stable. You cannot tell from one data point. With a year of data, you can identify which reports are gaining adoption, which are being abandoned, and which only appear during specific business cycles.

Before evaluating alternatives, it is worth being specific about what the native tooling covers.

The usage metrics report shows: total views per report, unique viewer count, views per day (30-day rolling), views by report page, views by platform (web vs mobile), and the top 10 most active users. The underlying dataset can be copied and queried directly — a data-savvy admin can build additional views on top of the existing 30-day slice.

The workspace-level admin monitoring workspace adds: feature usage and adoption metrics across the tenant, access logs, and user activity. This runs on audit log data and has a longer retention than workspace usage metrics, though the Microsoft documentation does not commit to a specific retention window for all event types.

For the tenant-level view, the Power BI REST API's Get-ActivityEvents endpoint surfaces activity events — report opens, dataset refreshes, dashboard views — with a 30-day history accessible via API. Teams with engineering resource can build their own retention by pulling these events daily and storing them in a SQL database or Azure Log Analytics workspace.

The gaps that remain regardless of approach: there is no native cross-workspace usage aggregation in a single view, no inactive report identification tool that scans the full tenant, and no signal connecting usage (who reads a report) to the pipeline reliability (whether the underlying data is current when they read it).

Third-party tools address the 30-day cliff by storing activity events they collect, rather than relying on Power BI's retention window.

The core mechanism is the same across tools: they connect to the Power BI REST API or the Activity Events endpoint, pull events on a regular schedule (typically every 5 to 60 minutes), and store them in their own database. Once stored, the data is yours regardless of what Power BI does with its own retention. The difference between tools is how long they retain data, what they build on top of it, and how much of the surrounding context they capture.

Usage history depth. SummitView retains usage data indefinitely. MetricSign retains 365 days. Both exceed Power BI's native 30-day window significantly. For most licence review and report retirement decisions, 365 days is sufficient. If you need to compare Q4 2025 usage against Q4 2024 to demonstrate year-over-year adoption growth, you need more than 12 months — SummitView's unlimited retention covers this; MetricSign's 365-day window covers most cases but not multi-year comparisons.

Inactive report detection. Both tools surface reports that have not been viewed in a configurable window. This is the core tool for report catalogue clean-up: identify assets with no views in 90 days, cross-reference against the governance owner, and initiate a retirement process. Without automated detection, this work is manual — a periodic CSV export and a spreadsheet.

Cross-workspace aggregation. Native Power BI shows usage per workspace. A report that exists in 4 workspaces (development, UAT, production, and a shared analytics workspace) shows fragmented usage in the native view. Dedicated tools aggregate across workspaces and, in some cases, correlate views of the same report across environments.

User-level activity. Understanding not just that 50 people viewed a report, but which people, when, and with what frequency, is important for licence review. Both SummitView and MetricSign retain user-level activity beyond 30 days.

One dimension that usage-only tools miss: usage data and reliability data exist in separate views.

A report viewed 400 times per month is more critical than one viewed 5 times. When it fails, the impact is larger — more users see stale or broken data. An intelligent monitoring layer should weight its alerts by usage: a refresh failure on a high-traffic report is more urgent than a failure on a report nobody opens.

This is not just a nice-to-have. The opposite condition is equally important: a report that refreshes successfully every day but has had no views in 90 days is consuming capacity without providing value. Identifying these 'high-cost/low-use' assets — successfully refreshing but unused — requires combining usage data with refresh history.

SummitView surfaces this in its Governance module under 'High-Cost/Low-Use' — a flag for datasets that are refreshing but unused. MetricSign surfaces usage alongside incident history, so you can see whether a dataset that failed last week is actually used by anyone.

For teams making capacity or licence decisions, this combination — who uses what, how reliably it runs, and what it costs in capacity — is the complete picture. Usage metrics alone answer the 'who uses it' question. Monitoring alone answers the 'is it working' question. The intersection is where decisions get made.

If you want to extend Power BI usage data without immediately deploying a third-party tool, two approaches work.

Option 1 — Daily export to SQL or Azure. Set up a Power Automate flow or Azure Function that calls Get-ActivityEvents daily and appends results to a SQL Server table or Azure Blob Storage. You build your own retention at the cost of ongoing maintenance. This works well for teams with engineering resource and works less well when Power BI's API changes or when you need cross-workspace aggregation.

Option 2 — Use a dedicated tool's trial period. Both SummitView (14 days) and MetricSign (45 days) offer free trials with full access. Connect the tool, let it start collecting, and use the trial period to see what it captures. After the trial, you have a clear picture of what the tool adds relative to native metrics and whether the cost is justified.

The 45-day trial window for MetricSign is long enough to see a full month of usage patterns plus the edge of the next cycle — enough to make an informed decision about whether extended retention changes how your team makes report and licence decisions.