Power BI Report-Scoped Measures: 6 Patterns for Better Reports

Video by: Reid Havens

Report-scoped measures (aka report-specific measures) are one of the easiest ways to level up a Power BI report without turning your semantic model into a junk drawer.

In this video, I walk through my Visual & Report-Scoped Measures guide and show the art of the possible. No deep DAX theory, no data engineering detours, just practical patterns you can steal immediately.

We’ll cover:

• What “report-scoped” measures are (and when you should use them)

• Six practical patterns for enhancing report UX with measures

• Dynamic titles that respond to context

• Conditional formatting driven by measures (so visuals explain themselves)

• How to keep your model clean while still making reports more interactive and readable

If you build reports for humans (not just for your own amusement), this is a strong set of patterns to have in your toolkit.

Power BI Field Parameters vs Calculation Groups (Clear Comparison)

Video by: Reid Havens

In this video I walk through the Field Parameters vs Calculation Groups Guide from Analytic Endeavors and explain what these two powerful Power BI features actually do, how they differ, and when to use each one in your reports.

You’ll learn:

• What Field Parameters are and how they let your users choose what to show in visuals

• What Calculation Groups are and how they let you change how measures calculate dynamically

• The key decision points between using FP and CG

• How each feature affects report design, maintenance, and flexibility

This is a conceptual walkthrough, not a step-by-step build. The goal is to help you understand the behaviors and tradeoffs so you can pick the right tool for the problem you’re solving.

Data Modeling vs DAX in Power BI: Solving Problems the Right Way (with Markus Ehrenmuller-Jensen)

LIVESTREAM DATE/TIME 📅

February 6th 2026 - 9:30AM (Pacific Time)

DESCRIPTION 📄

When should you solve a problem in the data model, and when should you solve it with DAX?

In this livestream, Markus Ehrenmuller-Jensen will walk through practical scenarios where you can take either route: modeling changes, DAX measures, or even report-level settings. Instead of “it depends,” you’ll see how to choose a clear, maintainable approach.

In this session, we discuss:

• How a solid data model can simplify or eliminate complex DAX

• When it actually makes sense to push logic into DAX instead of the model

• Typical patterns: role-playing dimensions, cascading filters, synced slicers, and more - Trade-offs around performance, maintainability, and usability

• How to think about long-term governance when mixing modeling and DAX solutions

Whether you’re a Power BI developer who loves writing measures or a data model purist, this session will help you decide where each piece of logic really belongs.

GUEST BIO 👤

​Markus Ehrenmuller-Jensen is the founder of Savory Data, with a career spanning project leadership, data engineering, and business intelligence architecture since 1994. He holds degrees in software engineering and business education and serves as a professor of databases and project engineering at HTL Leonding, a technical college. He is also certified in PL-300 (Power BI Data Analyst), DP-203 (Azure Data Engineer Associate Certification), DP-600 (Fabric Analytics Engineer Associate), and DP-700 (Fabric Data Engineer Associate).

Markus actively contributes to the global data community, speaking regularly at international conferences such as SQL Bits in London, Power BI Next Step in Copenhagen, Data Saturdays throughout Europe, and SQL Days. He co-founded SQL PASS Austria in 2013 and the Power Platform User Group Austria in 2016; both organizations merged in 2021 to form Data Community Austria. Since 2014, he has organized Data Community Austria Day in Vienna, fostering knowledge sharing among data professionals. In recognition of his technical leadership and community involvement, Markus has been honored as a Microsoft Data Platform Most Valuable Professional (MVP) since 2017.​

In addition to his speaking engagements, Markus contributes articles to reputable journals and has authored the book "Data Modeling with Microsoft Power BI," published in June 2024.

RELATED CONTENT 🔗

Markus's Website
Markus on LinkedIn
Markus on Bluesky
Data Modeling with Power BI (O’Reilly)
Selection2List DAX Package
Markus's GitHub

This One Change Made My Time-Intelligence [DAX] Performance Faster!

Video by: Reid Havens

Microsoft's new calendar-based time intelligence isn't magic, but it's a necessary optimization for every new model (and existing ones with performance issues).

The Results:
✅ Simplified query plans
✅ Fewer rows scanned by Storage Engine (SE)
✅ Same DAX, better syntax

The performance gains come from smarter query optimization. If you're working with large fact tables and time-based calculations, this is how the engine should be working.

What I cover:

• How calendar-based functions optimize the storage engine

• DAX Studio query plan comparison (side-by-side)

• Setting up calendar categories in Power BI

• Migrating from classic DATEADD to calendar-based syntax

• FREE Power BI Calendar Template (PBIX)

RELATED CONTENT 🔗

Calendar Template
Livestream - PBI Calendar Overhaul: Calendar-Based Time Intelligence (with Jeroen [Jay] ter Heerdt)
Calendar-based time intelligence
Microsoft Blog Announcement
Introducing calendar-based time intelligence in DAX