What is Power BI?
Power BI is Microsoft’s business intelligence platform for modeling, analyzing, and visualizing data. In web analytics, teams use it to combine behavioral metrics like Pageview, Session, and Event or Custom Events with marketing and revenue data to answer real business questions. Unlike tool-specific reports (GA4, Plausible, Matomo, Simple Analytics), Power BI lets you merge multiple sources—ad spend, CRM, subscriptions—into one governed, reusable model.
How does Power BI work?
Power BI has three core pieces:
- Desktop (modeling & DAX): Define relationships, measures, and business logic (e.g., mapping events to Conversion, Macro-Conversion, or Micro-Conversion).
- Service (sharing & refresh): Publish reports, schedule refreshes, and manage access for stakeholders.
- Gateways/dataflows: Centralize ETL and reuse curated tables across workspaces.
Typical web analytics workflow
- Collect & tag with your TMS so you emit Events, Pageviews, and campaign context (UTM, Campaign, Source, Referral, Referrer).
- Store data in a warehouse such as BigQuery or pull via APIs/CSV.
- Model KPIs and logic: Conversion Rate, Engaged Sessions, Attribution and Attribution Model, cohorts for Cohort Analysis.
- Visualize user journeys (User Flow), monitor Real-Time Data, and calculate performance/efficiency (ROI).
Power BI vs Google Data Studio
| Use case | Power BI | Google Data Studio |
|---|---|---|
| Data modeling | Robust semantic model, DAX | Lightweight calculated fields |
| Large/complex data | Handles big, related tables | Best for simpler datasets |
| Distribution | Managed workspaces & refresh | Quick link-based sharing |
When to choose it
- You need cross-tool reporting (e.g., event data + ad cost + subscriptions).
- You require governed definitions for KPIs across teams.
- You plan to iterate on advanced models (attribution, cohorting, LTV).