Managing a large Power BI environment is not just about reports and dashboards. It is like grooming a functional garden. Paths must be clear, plants must be well-kept, and "wild plants" must not spoil the inventory. In Power BI, these "wild plants" are unused or mismanaged reports, unclear permissions, and missing historical insights.
To keep your Power BI environment clean and productive, a custom monitoring solution using REST APIs is essential.
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Before reading further, check out the session where Michael Deepski demonstrates API monitoring in action and shows real-world use cases:
The Limitations of Native Monitoring
Power BI offers built-in usage metrics, but they are often not enough for enterprise governance. Some of the key limitations are:
- No Historical Data: Native reports show only the last 30 days. Year-over-year trends or month-over-month analysis is impossible.
- The "Black Box" Problem: It is not clear how native metrics are calculated, so admins must trust data without full transparency.
- Fragmented Access Control: Permissions can exist at individual, group, application, or workspace levels. Admins cannot easily see who belongs to dynamic Entra ID groups without external tools.
Building a Modern Monitoring Architecture
To overcome these limits, a custom data warehouse should be built. The recommended architecture includes:
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Data Sources
Combine Power BI/Fabric REST APIs for activity events and metadata with the Graph API to get user licenses and Entra ID group memberships.
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Orchestration
Use tools like Azure Data Factory or Airflow to manage ETL processes.
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Storage
Store the processed data in a SQL Database or a Fabric Lakehouse using a Medallion Architecture with Bronze, Silver, and Gold layers.
Tip: Use the Get Scan Results API to retrieve all objects in a workspace in a single run. This helps stay within API limits.
Structuring the Monitoring Data Warehouse
A star-schema data model is ideal. Important fact tables include:
- Fact Activity Events: Tracks every user action, such as downloading, sharing, or commenting.
- Fact Accesses: Daily snapshots of who has access to what and their roles.
- Fact Lineage: Shows how objects are connected for impact analysis when sources change.
- Fact Refreshes: Monitors duration and failure rates of semantic model refreshes.
Driving Governance, Adoption, and ROI
1. Proactive Governance
With API-driven monitoring, admins can:
- Identify unused reports and back them up or delete them.
- Validate Row Level Security (RLS) with a centralized view of DAX filters and roles.
2. Measuring Real Adoption
Licenses do not equal usage. Focus on real adoption metrics:
- Certified Object Usage: Track how much users rely on approved objects.
- Identifying Champions: Find advanced users who can train others.
- Self-Service Migration: Move popular reports from personal workspaces to the Enterprise zone.
3. Financial Optimization
Merge usage data with license info to create show-back or charge-back reports.
Example: One organization saved $30,000 annually by identifying 200 inactive Premium Per User licenses and deactivating them.
Conclusion
A custom API-driven monitoring solution transforms Power BI from a chaotic garden into a high-value corporate asset. Capturing historical data and combining it with user information allows organizations to prioritize the right assets, optimize license spending, and improve adoption.
Analogy for Understanding: Native Power BI monitoring is like a security camera that only keeps 30 days of footage and does not show who is behind a mask. A custom API warehouse is like a permanent, high-definition archive. It identifies everyone by name, tracks every tool’s usage, and helps you stop paying for tools that are just gathering dust.
🌍 Continuing the Journey
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