How to Implement Business Logic in Power BI?
At Helborg, we are constantly looking for ways to create the most effective and dynamic reports for our customers—reports that transform data into a powerful tool for executive decision-making. Over the years, we’ve encountered various industry beliefs related to Power BI, and through experience, we’ve formed our own perspective. One common belief is that as little logic and computation as possible should be performed in Power BI itself.
But is this really the case? Or could all calculations be handled within Power BI? Here are our recommendations:
Is It Risky to Add Business Logic in Power BI?
When designing a solution, one reason why companies avoid performing calculations and logic in Power BI is the fear that solutions will be limited to a single model—essentially a “black hole” for data.
However, this risk is minimal if the foundation of the data and data model is well-structured. In fact, implementing calculations and logic within Power BI can make the data much more dynamic and versatile, as Power BI’s measures allow for flexible data integration and the creation of business logic.
Another commonly mentioned risk is that importing raw data into Power BI may slow down reports—especially if the data model and measures are inefficiently built. This is a crucial factor to consider when designing reports, ensuring a balance between comprehensive and fast-access data for business needs.
When Should Logic Be Handled Entirely in the Database?
When calculations and logic are performed at the database level, report creation in Power BI becomes faster and simpler. If your goal is to create a basic and static report that only calculates sums and averages, pre-calculated data can be a practical solution.
However, if your goal is to leverage data in a more versatile way and derive deeper business insights, such a static approach can be too limited.
The Most Effective Reporting Comes from a Hybrid Approach
As expected, the best Power BI reporting solutions come from a combination of both approaches. Our recommendation is to aggregate some data and implement some logic in the database—but only as much as necessary.
To put it simply:
If all logic is handled at the database level, reports often become too simplistic, serving mainly as data viewers.
If business logic is also implemented in Power BI, reports become interactive analytical tools that genuinely support business decision-making.
As Power BI experts, our job at Helborg is to guide customers on the best way to implement solutions based on their specific data and business needs. We’ve also found that Power BI is a great tool for identifying what kind of data a company actually needs.
At the beginning of a project, you don’t need to have a clear idea of what insights you want from the data. Instead, it’s beneficial to import as much relevant data as possible, allowing you to analyze it against business requirements. As reporting needs become clearer, calculations can always be shifted to the database later.
Example: Defining Calculations and Logic
At the start of a project, basic reporting needs are often well-defined, such as monthly sales figures by business unit, team, or product. However, these insights are usually not deep enough for business decision-making. Companies typically need to explore further, asking questions such as:
What do sales look like on a daily or weekly level?
Why are profit margins decreasing?
Should all products be sold in all regions?
Which customers are the most profitable?
How many customers are at risk of leaving?
What factors contribute to long-term customer relationships?
How has a price change impacted customer volume and revenue?
If reports were pre-aggregated in the database based on initial requirements, expanding the reporting scope would require rebuilding part or all of the data layer.
Instead, when data is stored at a granular level and calculations are primarily handled in Power BI, there’s no need to make major changes to the data layer later. This saves time and resources, as new, more advanced reports can be created using the original dataset, providing broader business insights.
What to Avoid in Power BI Queries
One thing to be cautious of is making significant data transformations within Power BI’s query editor. While this isn’t always avoidable, it can lead to slow model refreshes, even if the data volume isn’t large.
Some of the most performance-heavy operations in Power BI’s Query Editor include:
Append and Merge operations
Complex aggregations
These should generally be avoided when possible. However, smaller data modifications, row-level calculations, and simple data corrections are well-suited for Power BI’s Query Editor.
Looking for a Partner to Support Your Power BI Implementation and Development?
If you need expert guidance on adopting and optimizing Power BI for your business, we are here to help. Let’s build a reporting solution that truly serves your business needs—efficient, dynamic, and insightful.
Book a meeting with Eveliina Herttua, who is available to answer your questions. You can contact her through email, phone or LinkedIn:
Eveliina Herttua
+358 (0)50 533 7579
eveliina.herttua@evalkia.fi
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