From Raw Telemetry to Business Intelligence: Building a Custom Intune Analytics Pipeline

Building a custom business analytics engine for Microsoft Intune is a high-impact project for any modern IT leader. Native reports are great, but for a truly data-driven organization, you need to own your data.

By moving Intune telemetry into a dedicated Azure Database for MySQL and visualizing it with Grafana, you unlock historical trends and cross-platform insights that standard dashboards just can't match.

Here is a comprehensive guide on how to build this pipeline.

The Architecture at a Glance

To achieve this, we follow a four-stage "Extract-Transform-Load-Visualize" (ETLV) workflow:

1. Extract: Pull device and app data from the Microsoft Graph API.

2. Transform: Clean and normalize the data using Python (Pandas).

3. Load: Insert processed data into Azure Database for MySQL.

4. Visualize: Connect Grafana to MySQL for real-time dashboards.

Phase 1: Pulling Data from Microsoft Graph

To pull Intune data programmatically, you must first register an application in the Azure Portal (Microsoft Entra ID) to get your Client ID and Tenant ID.

Key API Endpoints:

• Managed Devices: GET https://graph.microsoft.com/v1.0/deviceManagement/managedDevices

• App Inventory: GET https://graph.microsoft.com/v1.0/deviceAppManagement/mobileApps

  
  

Phase 2: Data Science "Top Practices" for Cleaning

Raw API data is often "dirty"—it contains null values, inconsistent string casing, and nested JSON.

Best Practices applied via Pandas:

• Handling Missing Values: Replace NaN with "Unknown" or use mean-imputation for numerical metrics like "Battery Health."

• Structural Errors: Normalize casing (e.g., converting "ios" and "iOS" to a uniform "iOS").

• Deduplication: Ensure a device isn't counted twice if it was re-enrolled.

Phase 3: Loading into Azure MySQL

Now that the data is clean, we move it to your persistent storage. Using mysql-connector-python, we map our DataFrame to a MySQL table.

Pro-Tip: Use a "Upsert" (Update or Insert) logic based on the device_id so your database stays current without creating duplicate entries every time the script runs.

Phase 4: Meaningful Visualization in Grafana

Once your data is in MySQL, add it as a Data Source in Grafana using the native MySQL connector.

What makes a "Meaningful" Intune Dashboard?

Don't just count devices. Aim for Actionable Intelligence:

1. Security Posture: A "Gauge" panel showing the percentage of compliant vs. non-compliant devices.

2. OS Distribution: A "Pie Chart" showing the version breakdown (e.g., how many Macs are still on Monterey vs. Sonoma).

3. Sync Staleness: A "Bar Chart" showing devices that haven't checked in for 30+ days—these are your primary security risks.

4. Enrollment Trends: A "Time Series" graph showing new device enrollments over the last 6 months.

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Conclusion

By building this pipeline, you transition from being a "Tool Administrator" to a "Systems Architect." You aren't just managing devices; you are providing the business with a clear, visual narrative of their security and infrastructure health.

What’s next? You could integrate this with your GitLab CI/CD to run this Python "Data Sync" every 4 hours automatically!

check my python code to handle this https://github.com/j33tu/devops/blob/master/python/intune_devices_information.ipynb