Power BI: Fix 3 Common Mistakes

Key insights

• This YouTube video highlights three common Power BI mistakes and clear fixes for faster, cleaner reports.
  It focuses on data modeling, report performance, and user experience.
• Mistake: using calculated columns when you should use measures — calculated columns increase model size and slow queries.
  Fix: use measures for aggregations and do transformations in Power Query or at the source.
• Mistake: building flat tables instead of a star schema, which makes models hard to maintain and slow to query.
  Fix: separate fact and dimension tables, use stable keys, and avoid wide, flattened tables.
• Mistake: packing too many visuals on one page, which creates clutter and confuses users.
  Fix: give each page one clear message, limit visuals, and use drill-through, bookmarks, or navigation for details.
• New emphasis: treat the semantic model as a reusable asset and trim data before it reaches the model.
  Action: filter and shape data early, keep the model lean, and reuse measures and tables across reports.
• Benefits and quick best practices: better performance, easier maintainability, and greater reusability when you use measures, a star schema, and focused pages.
  Tip: plan the model first, limit visuals per page, and prefer query-time calculations over stored columns.

The newsroom reviewed a recent YouTube video by the channel Curbal that highlights three frequent mistakes Power BI users make and how to fix them. In straightforward terms, the video focuses on model design, report layout, and the proper use of DAX, and it frames each point with practical examples. Accordingly, this article summarizes the video for editors and readers, while explaining tradeoffs and common challenges. Moreover, it aims to give clear steps teams can apply immediately to improve performance and maintainability.

Video overview and main message

The presenter opens by naming three recurring problems: misuse of calculated columns, poor data modeling instead of a star schema, and crowded report pages. Then, the discussion moves from symptoms to remedies, showing how small design choices have large consequences. As a result, the video urges developers to treat the semantic layer as a reusable asset and to reduce unnecessary data before loading it. Therefore, the guidance shifts away from quick fixes toward sustainable architecture and governance.

Mistake 1 — calculated columns versus measures

The video stresses that many creators add calculated columns by default, which increases model size and can slow queries. In contrast, creators should prefer measures for aggregations because measures are evaluated at query time and do not bloat the model. However, the presenter explains a clear tradeoff: calculated columns are useful when you need row-level values for relationships or row-level security, while measures are better for dynamic calculations.

Practically, the presenter recommends moving static transformations to Power Query or the data source so the model stays lean. Yet, teams must balance pre-loading transformations with governance and refresh complexity, because pushing too much logic upstream can affect data pipeline performance. Consequently, adopting a rule of thumb — measures for aggregations, columns for keys — reduces risk but still requires judgment on a case-by-case basis.

Mistake 2 — flattening tables instead of building a star schema

The video advocates for separating fact tables and dimension tables rather than using a flat, wide table. This structure makes relationships explicit, simplifies DAX, and often improves performance, especially for large datasets. Yet, the presenter points out tradeoffs: converting to a star schema can require additional ETL effort and schema changes at the source, which may not be trivial for legacy systems.

Therefore, the recommended approach is incremental: prioritize splitting high-cardinality or frequently changing entities first, and document keys and relationships clearly. Teams should also consider the governance implications, because a cleaner model invites reuse across reports but demands disciplined naming and version control. Ultimately, the video frames the star schema as an investment that reduces long-term maintenance and increases analytical accuracy.

Mistake 3 — overloading report pages with visuals

According to the video, many reports become noisy when authors try to show every metric on a single page, which confuses users and hurts performance. Instead, the presenter suggests designing pages around a single message and using navigation, drill-through, or bookmarks to provide layered detail. This reduces visual clutter while allowing depth when needed, improving both user comprehension and rendering speed.

However, the tradeoff involves user discoverability: fewer visuals per page means readers may need guidance to find deeper insights. Consequently, the video recommends clear labels, consistent filters, and navigational hints so consumers can follow the analysis path. In practice, balancing simplicity and accessibility requires user testing and iterative improvements.

Newer guidance — treat the semantic model as an asset and minimize data early

Importantly, the video reflects recent community advice to treat the semantic model as reusable intellectual property instead of embedding logic purely in reports. This approach supports consistency across teams, speeds report creation, and enables centralized performance tuning. Meanwhile, the presenter emphasizes removing unneeded data before it enters the model to keep file sizes small and refresh times reasonable.

That said, centralizing the model brings governance challenges, such as deciding who owns changes and how versions are deployed. Teams must trade off flexibility for control, and they should implement clear deployment and approval processes. In short, reusable models pay dividends but require organizational coordination.

Practical steps, tradeoffs, and final recommendations

The video ends with actionable steps: prefer measures for aggregations, apply Power Query transformations before load, move toward a star schema, and design one-message pages with guided navigation. Additionally, it encourages treating DAX as a tool for dynamic calculations and avoiding unnecessary stored columns. Yet, the presenter reminds viewers that every environment has constraints, so teams must balance immediate needs with longer-term architecture goals.

For newsroom teams and analysts, the takeaway is clear: small modeling and design choices compound quickly, so introduce standards, review models periodically, and involve both data engineers and report authors in decisions. By doing so, organizations can improve performance, maintainability, and user trust, while accepting that certain tradeoffs will require ongoing attention. Overall, the video by Curbal offers a practical roadmap for avoiding the most common Power BI pitfalls while acknowledging real-world constraints.

Keywords

Power BI mistakes, common Power BI mistakes, Power BI best practices, Power BI performance optimization, Power BI DAX mistakes, Power BI data modeling mistakes, Power BI visualization tips, fix Power BI errors