If your Power BI reports take minutes to load, your team has probably started working around them. They export to Excel instead. They stop hitting refresh. They quietly lose trust in the numbers. Power BI performance optimisation isn't just a technical challenge. It's a business problem, and it's one we see regularly with Brisbane and South East Queensland businesses of all sizes.
Why Power BI Gets Slow in the First Place
Most slow reports don't start out that way. They slow down gradually as more data gets added, more visuals get built, and more users pile in. Sound familiar?
The culprit is usually one of three things: a bloated data model, poorly written DAX measures, or too many visuals firing queries at the same time. Sometimes it's all three. The good news is that each has a clear fix once you know where to look.
Brisbane businesses running Power BI on top of large SQL databases or cloud data warehouses tend to hit this wall faster than they expect. The report works fine in development with a trimmed-down dataset. Then it slows to a crawl in production when two or three years of transactional data are sitting behind it. That's not a Power BI problem. That's a design problem.
The problem also compounds over time. A report built for 50,000 rows struggles when the underlying table grows to five million. A dashboard designed for 10 users creaks under 100 concurrent ones. Most performance issues aren't bugs. They're the result of growth that the original build wasn't designed to handle.
Start With Your Data Model
The data model is the foundation of every Power BI report. If it's built poorly, no amount of tweaking visuals or rewriting DAX will get you the performance you need. We've seen models with hundreds of columns across dozens of tables where the report only uses 40 of them. That overhead adds up fast.
The most common model problems we see are imported columns nobody uses, tables brought in at the wrong grain, and relationships set up incorrectly. Every unnecessary column adds to your model size and memory footprint. Every row that shouldn't be there slows down every query against that table.
- Remove unused columns before importing data, not after
- Aggregate data to the right grain for your reporting needs
- Use integers for relationship keys instead of text strings
- Turn off Auto Date/Time if you have your own date table
- Set storage mode to Import rather than DirectQuery where your data volume allows
These aren't glamorous fixes. But they're the foundation of any solid Power BI performance optimisation effort, and they make the biggest difference in practice. A well-structured model can cut report load times from 30 seconds to under three. Your users will notice immediately.
Write DAX That Performs Under Pressure
DAX is where a lot of Power BI performance problems live. A poorly written measure can turn a simple card visual into a query that runs for 30 seconds. The report looks fine to the developer working on a small sample dataset. It punishes the end user on a production environment with millions of rows behind it.
The biggest DAX mistake we see is using CALCULATE with overly complex filter contexts when a simpler measure would do the same job. Another common one is nesting iterator functions inside each other, like SUMX inside AVERAGEX, when a pre-aggregated column in your model would perform far better. Complex DAX isn't always better DAX.
If you're not sure where your slow queries are coming from, open Performance Analyser inside Power BI Desktop. It shows you exactly how long each visual takes to render and which DAX queries are the real culprits. That's your starting point for identifying what actually needs fixing.
When the Problem Is Bigger Than a Few Fixes
Sometimes a report is slow because it was built without performance in mind from day one. In those cases, patching individual measures or removing a few columns won't be enough. You need to look honestly at whether the underlying model architecture can actually scale.
This doesn't always mean starting from scratch. But it does mean being honest about how much technical debt has built up. We've written about when to rebuild Power BI reports versus patching them, and the answer usually comes down to how far the report has drifted from solid design principles.
Ask yourself these questions. Is the model growing faster than your team can manage? Are refresh times creeping past what users will tolerate? Are people going to Excel or other tools for answers instead? If yes to any of those, a structured performance review is overdue. A good review will tell you clearly whether you need targeted fixes or a full redesign.
Power BI performance optimisation is a specialty, not an afterthought. At Roar Data, we work with Brisbane businesses to diagnose slow reports, restructure data models, and write DAX that scales with your data. If your reports are frustrating your team or taking too long to refresh, our Power BI performance optimisation service is a practical place to start. Get in touch and we'll show you exactly what's slowing things down.