Model Your Data in Power BI: Star Schemas, Filter Flow & Field Lists

Video by: Reid Havens

Plenty of Power BI issues that look like DAX issues start one layer down, in the model. The formula is correct, the relationship underneath it isn't doing what you assumed, and you spend an afternoon rewriting a measure that was never the problem.

This one covers the foundation the rest of it sits on. Why a model beats one giant flat table (smaller storage, better compression, faster refresh, and filter paths you get without writing any code), how dimensions and facts split the who-what-when-where from the numeric events, and what actually happens when someone picks a value in a slicer and that filter travels down the relationship into the fact table.

Then star versus snowflake, compared on the four things that decide it in practice: query performance, DirectQuery latency, how long the DAX ends up, and whether business users can find a field without asking someone. Star wins on all four. I also get into why the snowflake version keeps turning up anyway, since it usually arrives from a source system rather than from a decision anyone made.

The last section is the one that gets skipped. Every visual in Power BI generates a query with three ingredients (filter, group, summarize), and once that clicks, a wrong number stops being mysterious. After that it's the fields list itself: friendly names instead of cgo_key_fk, display folders, hidden keys, and descriptions that surface as tooltips. It sounds cosmetic, but an unreadable fields list is how you end up with six competing Excel copies of the same report.

Simplify Power BI DAX with Visual Calculations

Video by: Reid Havens

If you've ever written a running total in DAX, you know it's more work than it has any right to be. CALCULATE, FILTER, a column reference you have to get exactly right, and a quiet prayer that it behaves at the subtotal level. Visual calculations let you write RUNNINGSUM([Sales]) and get on with your day.

They're a calculation layer that lives inside the visual itself, working on the data that's already been aggregated rather than querying the whole model. That's also why they're often faster, sometimes dramatically so on DirectQuery.

I walk through the core functions (running totals, moving averages, PREVIOUS and NEXT for period comparisons, FIRST, LAST, INDEX, and RANK), then the hierarchy functions like COLLAPSE and EXPAND that make "% of parent" and "% of grand total" almost trivial. We also get into AXIS and RESET, which control the direction a calculation runs and where it restarts, because that's the part that quietly breaks people's running totals across years.

One honest caveat. Yes, they're easier and faster. But because each one only lives in a single visual, they can fragment your business logic across reports if you're not careful, so I cover when a visual calc is the right call and when it really should be a centralized measure.

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.