University > Power BI course > Semantic Model & Data Modeling > Modeling mistakes and how to fix

Common Power BI modeling mistakes (and how to fix them)

Learn the most common Power BI data modeling mistakes in dashboards and how to fix them to improve performance, reliability, and KPI accuracy.

Semantic Model & Data Modeling

When Power BI dashboards feel slow, inconsistent, or hard to trust, the root cause is often not the visuals or the formulas, but the data model itself. Modeling mistakes are common, especially when marketing teams start combining multiple platforms, time dimensions, and KPIs.

This article walks through the most frequent Power BI modeling issues encountered in marketing reporting, explains why they cause problems, and shows how to fix them with simple, practical principles.

Using the wrong level of granularity

One of the most common modeling mistakes is working at the wrong level of detail. Fact tables are sometimes built at a very granular level, such as keyword, ad, or user, even though reporting needs are focused on campaigns, channels, or weeks. In other cases, data is aggregated too early, removing the ability to drill down when needed.

When granularity does not match reporting needs, dashboards become slower, visuals more complex, and KPIs harder to interpret.

Fix it by aligning the dataset with the level you actually need to analyze.

Decide the reporting grain first (daily, weekly, campaign, channel).
Avoid mixing multiple grains in the same table (for example, campaign rows mixed with keyword rows).
Aggregate upstream only when it improves clarity and performance, and keep raw detail only if you truly need drill-down.

Too many columns in fact tables

Fact tables should focus on metrics and keys, but they often end up overloaded with descriptive fields such as names, labels, categories, or textual attributes. This makes the model harder to read and negatively impacts performance.

Large fact tables with many descriptive columns also make it more difficult to manage relationships and increase the risk of duplicated logic across reports.

Fix it by keeping fact tables lean and moving descriptive context into dimensions.

Keep only metrics and keys in the fact table (clicks, cost, conversions + IDs).
Move names, labels, categories, and descriptions into dedicated dimension tables.
Remove unused columns early to improve model clarity and performance.

Duplicate or unstable keys

Relationships rely on keys being unique and stable. Problems appear when campaign IDs are duplicated, when joins are based on names instead of IDs, or when keys change across exports or platforms.

These issues often result in broken relationships, duplicated metrics, or filters that behave unpredictably.

Fix it by using stable, unique keys so relationships and filters behave predictably.

Prefer IDs over names for joins (campaign ID is safer than campaign name).
Clean and standardize keys in Power Query (trimming, casing, consistent formats).
Ensure the “one” side of relationships is truly unique before building the relationship

Many-to-many relationship chaos

Many-to-many relationships are sometimes introduced to “fix” numbers quickly, but they often create more problems than they solve. Filters can propagate in unexpected ways, metrics can be duplicated, and dashboards become difficult to debug.

In marketing models, many-to-many relationships usually indicate missing dimension tables or incorrect granularity.

Fix it by restructuring the model to return to simple one-to-many relationships.

Avoid linking fact tables directly when possible.
Introduce proper dimension tables (Campaign, Date, Channel, Geo) to connect facts cleanly.
Keep single-direction filtering by default and only change it with a clear reason.

Performance issues caused by modeling choices

Slow dashboards and long refresh times are often the result of modeling decisions rather than data volume alone. Common causes include too many columns, unnecessary relationships, bidirectional filtering everywhere, or high-cardinality text fields in fact tables.

Fix it by simplifying the model so Power BI can optimize it efficiently.

Reduce wide tables and high-cardinality text fields, especially in fact tables.
Limit the number of relationships and avoid unnecessary bidirectional filters.
Aggregate data when it improves usability and keeps dashboards responsive.

Modeling mistakes recap (marketing perspective)

The table below summarizes the most common Power BI modeling mistakes, their impact on marketing dashboards, and how to fix them.

Modeling mistake	What it looks like in practice	Impact on marketing dashboards	How to fix it
Wrong granularity	Data too detailed or aggregated too early	Slow reports, unclear KPIs	Choose the correct analysis level upfront
Too many columns in fact tables	Descriptive fields mixed with metrics	Confusing model, poor performance	Move attributes to dimension tables
Duplicate or unstable keys	IDs not unique, joins based on names	Broken relationships, duplicated metrics	Use stable business keys and clean them
Many-to-many relationships	Fact tables linked together	Unpredictable filters, inflated KPIs	Introduce dimensions and use one-to-many
Performance-heavy modeling	Bidirectional filters, large text fields	Slow refresh and interactions	Simplify the model and reduce complexity

Conclusion

Most Power BI issues are modeling issues. When the structure is wrong, dashboards become fragile, KPIs lose trust, and maintenance effort grows quickly.

For marketing teams, simple models almost always outperform complex ones. Clear granularity, clean keys, proper dimensions, and predictable relationships go a long way toward building dashboards that are fast, reliable, and scalable over time.

Table of content