Power BI usage monitoring requirements

Direct answer: A production Power BI usage-monitoring implementation needs an agreed scope, supported data sources, a Microsoft identity, tenant and workspace permissions, approved SQL storage, scheduled compute, network access, retention rules, validation, monitoring, documentation, and named operational owners. The report is the last layer—not the first requirement.

This checklist is for a maintained monitoring process, whether your team builds it or deploys a product. It is intentionally more demanding than opening Power BI’s native Usage Metrics report for one workspace. If the native experience answers the question, use it.

Power BI usage monitoring requirements checklist

Area	Minimum decision	Evidence of readiness
Scope	Which workspaces, item types, users, and decisions are included?	Approved scope list and accountable sponsor
Sources	Which Microsoft source answers each metric?	Source-to-metric definition with limitations
Identity	Delegated account or service principal?	Created identity, owner, backup owner, credential policy
Permissions	Which tenant settings, API scopes, and workspace roles are required?	Administrator approval and tested access
Storage	Where will raw and modeled history live?	Database, schema, encryption, backup, and retention approval
Compute	What runs collection, transformation, and validation?	Approved runtime and deployment path
Operations	Who responds when collection fails or Microsoft changes a source?	Monitoring, runbook, technical owner, backup owner
Consumption	Who can see identifiable usage and how will they act?	Access model, report audience, decision workflow

1. Define the decision and scope

Do not begin with “collect everything.” Begin with the decision: adoption review, performance prioritization, consolidation, owner outreach, or retirement. Then identify the selected workspaces and activity needed to support it.

Record explicit exclusions such as My Workspace, unsupported item types, unapproved workspaces, or tenant-wide governance use cases beyond the implementation’s public scope. Scope controls cost, permissions, privacy, validation effort, and operational load.

2. Map each metric to a supported source

Power BI inventory, activity, page behavior, viewer identity, and performance are not one universal dataset. Microsoft documents meaningful differences between Usage Metrics, activity logs, and admin APIs.

Inventory establishes what workspaces and items exist now.
Activity events record supported user and administrative operations.
Usage Metrics provides workspace usage and, depending on the experience, page and performance signals.
Business-owner and criticality fields usually come from your own process.

For every measure, document the source, definition, window, time zone, known exclusions, and validation method. Review the current Power BI Usage Metrics limitations before promising coverage.

3. Choose and govern the Microsoft identity

A scheduled process needs an identity that can authenticate without depending on one employee’s interactive session. Depending on the APIs and scope, that might be a service principal or a controlled delegated account.

Microsoft’s tenant-level auditing guidance describes using a Microsoft Entra app registration and service principal for read-only admin APIs. It also documents a tenant setting that allows approved service principals to call those APIs. Metadata scanning can alternatively use delegated administrator access.

Record:

application and tenant identifiers;
the security group controlling allowed service principals;
credential type, secure location, expiration, and rotation owner;
primary and backup technical owners;
the exact API and workspace access granted.

Least privilege still requires testing

A permissions diagram is not proof that collection works. Run a bounded proof of concept against representative workspaces, item types, sharing paths, and privacy settings before approving production.

4. Configure tenant and workspace access

Administrator settings control access to usage metrics, per-user data, service-principal admin APIs, and optional detailed metadata scanning. Workspace roles can also determine whether the identity can retrieve a particular dataset.

Create an approval record that states why identifiable viewer activity is needed, who can read it, and what happens when the organization disables per-user data. The model should degrade honestly—showing masked or unavailable identity—not silently invent a complete audience count.

5. Approve SQL storage and retention

History needs more than a connection string. Define:

server, database, schema, and region;
network path and firewall rules;
encryption in transit and at rest;
service identity and least-privilege database permissions;
raw, staged, and modeled retention;
backup, restore, and disaster-recovery expectations;
handling for user identifiers and deletion requests.

Separate raw-source retention from analytical retention. Raw events help with reprocessing and source-change investigations; modeled facts support reporting. They may need different access and lifecycle policies.

6. Provide scheduled compute and networking

The collector needs a reliable runtime: a customer-approved VM, container platform, automation service, or equivalent compute. It must reach Microsoft endpoints, the SQL environment, identity services, and any package or secret source required by the deployment.

Document proxy behavior, private networking, DNS, TLS inspection, outbound allowlists, and runtime time zone. Page and performance telemetry can also be affected by client-side network rules; do not confuse that source limitation with collector connectivity.

7. Design collection for limits and change

Microsoft documents request limits for several admin operations. The activity-events operation, for example, is limited to 200 requests per hour, and Microsoft warns that repeated loops increase throttling risk. The preview unused-artifacts operation is also limited to 200 requests per hour.

A production process should implement bounded concurrency, continuation tokens, retries with backoff, idempotent writes, watermarking, and late-arrival handling. It should preserve raw responses when permitted so a mapping change does not always require recollection.

8. Add validation before the dashboard

Validate at three levels:

Collection health: Did every expected workspace and interval run successfully?
Data quality: Are identifiers, timestamps, counts, and relationships plausible and complete within the source boundary?
Business acceptance: Do representative owners recognize the reports, viewers, and trends?

Publish the last successful collection time and scope beside the analytical pages. A polished dashboard backed by a partial load is worse than an explicit failure.

9. Assign operational ownership

Microsoft’s auditing guidance recommends a technical owner and backup owner because APIs and requirements can change. Add an owner for the analytical definitions as well. The person maintaining authentication might not be authorized to redefine “active report.”

Your runbook should cover credential expiry, API throttling, new event fields, removed workspaces, schema changes, failed schedules, database capacity, and source discrepancies.

10. Define the decision workflow

Monitoring creates evidence. Someone still has to act. Decide who reviews declining adoption, who contacts report owners, who approves archive or retirement, and how actions are recorded.

For retirement specifically, pair monitoring with the retirement checklist and the guide to finding unused reports across workspaces.

Primary sources

Pressure-test your readiness before implementation

UsageVault includes the collector, SQL objects, scheduling, validation, documentation, handoff, and a Power BI decision report—but it still needs approved customer infrastructure and administrators.

Review implementation requirements →