A great Power BI report lives or dies by its data model. Even the most visually polished dashboard will fail if the underlying model is slow, inconsistent, or logically flawed. Unfortunately, Power BI data modelling mistakes are far more common than most organisations realise.
In fact, we regularly see capable Power BI teams struggling with slow refresh times, incorrect totals, duplicated results, and confusing filters. In almost every case, the root cause is not the visuals, it’s the data model underneath.
This article walks through the most common Power BI data modelling mistakes we see at AGER BI, explain why they cause problems, and, most importantly, show you how to fix them. Whether you’re building a single dashboard or an enterprise BI platform, these principles help create clean, fast, and scalable models.
1. Using a flat table instead of a star schema
The mistake
One of the most common Power BI data modelling mistakes is loading a single, massive “everything” table into Power BI. At first glance, this approach seems simple. All the data is in one place, and you can start building visuals quickly.
However, as datasets grow, this structure quickly breaks down. Performance degrades, calculations become fragile, and even small logic changes can cause unexpected results.
The fix
Instead, adopt a star schema, which is the recommended best practice for Power BI reporting.
Star schema consists of:
- Fact tables that store measurable data, such as sales, revenue, or transactions
- Dimension tables that provide context, such as customers, products, dates, or locations
To optimise your model:
- Use one-to-many relationships from dimensions to facts
- Set relationships to single direction wherever possible
- Apply consistent naming conventions, such as Fact_Sales and Dim_Customer
As a result, your DAX becomes simpler, query performance improves dramatically, and your reports remain responsive even with millions of rows.
2. Overuse of bidirectional relationships
The mistake
Another frequent Power BI data modelling mistake is enabling bidirectional relationships to “make filters work”. While this may appear to solve immediate issues, it often introduces far bigger problems down the line.
Bidirectional filtering can cause:
- Ambiguous filter paths
- Circular dependencies
- Incorrect totals that are difficult to trace
- Slower report performance
The fix
As a rule of thumb:
- Use single-direction relationships by default
- Allow filters to flow from dimensions to facts
When you genuinely need more complex behaviour, use DAX techniques such as:
- USERELATIONSHIP() for inactive relationships
- TREATAS() to apply virtual relationships in measures
Additionally, documenting your model becomes essential when exceptions exist. This ensures future developers understand the intended filter flow rather than guessing.
3. Putting calculations in the wrong layer
The mistake
Many teams push too much logic into Power BI, performing heavy transformations and calculations inside the report layer. While Power BI is powerful, this approach often leads to slow refresh times and bloated models.
The fix
Follow a clear ETL separation of concerns:
- Extract and transform data upstream in Power Query, Azure Data Factory, or your data warehouse
- Model relationships in Power BI
- Calculate only what is needed for reporting using measures
With transformations handled upstream, dataset size is reduced, performance refresh improves, and keep your DAX focused on analytics rather than data clean-up.
4. Ignoring data types and formatting
The mistake
Relying on Power BI’s auto-detection for data types is another subtle but costly Power BI data modelling mistake. Incorrect data types can lead to inefficient compression, broken relationships, and misleading visuals.
The fix
Before loading data:
- Explicitly define data types, such as Date, Whole Number, or Decimal
- Standardise key columns across tables (e.g. matching date formats)
- Disable “Auto Detect Relationships” to prevent hidden or incorrect joins
Consistent data types improve both performance and reliability, particularly in larger models.
5. Overusing calculated columns
The mistake
Calculated columns are often used where measures would be more appropriate. While calculated columns can be useful, they are stored in memory and increase the size of your dataset.
Over time, this leads to slower refreshes and unnecessary memory consumption.
The fix
To optimise your model:
- Prefer measures for dynamic calculations
- Use calculated columns only for static values that never change
- Regularly audit your model and remove unused or redundant columns
This approach keeps your model lean while ensuring calculations remain accurate and flexible.
6. Not using incremental refresh
The mistake
Refreshing an entire dataset every time, even when only new data has changed, is a major Power BI data modelling mistake, especially for large historical datasets.
The fix
Implement incremental refresh to:
- Partition large fact tables by date
- Refresh only recent data while keeping historical data static
- Reduce refresh times from hours to minutes
When combined with tools like Azure Data Factory, incremental refresh also lowers compute costs. This improves overall reliability.
7. Failing to document the data model
The mistake
Relying on memory or “tribal knowledge” to understand a Power BI model is risky. When the original developer leaves or the model grows, confusion and errors are inevitable.
The fix
Adopt lightweight but effective documentation, including:
- The purpose of each table
- Key relationships and assumptions
- Important DAX measures and logic
- Ownership and maintenance responsibilities
Using tools like SharePoint or Git for version control ensures your Power BI environment remains sustainable over time.
How AGER BI helps
At AGER BI, we specialise in designing, deploying, and optimising Power BI–led analytics platforms on Azure that are built for performance and scalability. We focus on getting the data model right. We know that most Power BI issues stem from poor modelling decisions made early on.
We don’t just deliver dashboards. We design analytics foundations that eliminate common Power BI data modelling mistakes and ensure your reports remain fast, accurate, and easy to maintain as your organisation grows.
What You Get with AGER BI
Robust, future-proof Power BI data models
We design clean star schemas, well-defined relationships, and scalable semantic models. These support complex reporting without sacrificing performance or accuracy.
Azure-native architecture optimised for Power BI
From Azure SQL and Synapse to Data Factory and Lakehouse patterns, we design platforms that integrate with Power BI seamlessly and scale efficiently. We do this as data volumes increase.
Cost-optimised pipelines and storage
We eliminate unnecessary transformations, duplicated datasets, and inefficient refresh strategies. This helps you avoid common Azure and Power BI cost traps while maximising value.
Governed, trusted analytics
We implement proper data validation, security, and documentation so stakeholders can trust the numbers and analysts can confidently extend the model without breaking it.
Fast, intuitive Power BI reporting
Our models are designed to support responsive visuals and simple DAX. This enables decision-makers to explore insights quickly without waiting on slow refreshes or confusing filters.
End-to-end delivery
From data ingestion and transformation through to modelling, measures, and final reports, we ensure every layer of your Power BI solution works together as one cohesive system.
Your Advantage
Whether you’re fixing an underperforming Power BI model, modernising an existing data warehouse, or building a cloud-native analytics platform from the ground up, AGER BI helps you avoid costly Power BI data modelling mistakes before they impact your business.
By combining deep Power BI expertise with proven Azure architecture patterns, we reduce delivery risk, accelerate time to insight, and ensure your analytics investment delivers measurable, long-term value.
Conclusion
Power BI data modelling is both an art and a discipline. While Power BI makes it easy to build reports quickly, long-term success depends on avoiding these common Power BI data modelling mistakes.
By adopting star schemas, managing relationships carefully, placing logic in the right layers, and documenting your models, you create dashboards that are fast, accurate, and scalable.
If your Power BI reports feel slow, inconsistent, or difficult to maintain, AGER BI can audit and optimise your data models to unlock their full potential.
