Day 16: Explicit Measures vs Implicit Measures in Power BI

"Choosing between implicit and explicit measures is like choosing between speed and customization in Power BI - it all depends on the specific needs of your data visualization."

As a Dax Expert and Power BI Trainer, I often get asked about the difference between Implicit Measures and Explicit Measures in Power BI. Both Implicit Measures and Explicit Measures are important in creating effective and insightful visualizations, but they have distinct differences and uses. In this blog, I will explain the differences between the two types of measures, their advantages and disadvantages, and provide examples of each.

Implicit Measures in Power BI

Implicit measures in Power BI are measures that are automatically created by Power BI based on the data model. Power BI generates implicit measures based on the aggregation used in the visualizations. For example, if you create a bar chart and drag a field to the Value area, Power BI automatically creates a sum aggregation for that field, which becomes an implicit measure. Implicit measures are also created when you use the Quick Measure feature in Power BI.

Advantages of Implicit Measures:

• They are automatically created by Power BI, which saves time and effort.
• They are easy to create, as they do not require any DAX formula writing.
• They work well for simple aggregations, such as sum or count.

Disadvantages of Implicit Measures:

• They have limited functionality as they are pre-defined by Power BI.
• They may not provide the level of complexity needed for more advanced calculations.
• They can be difficult to modify or customize as they are created automatically.

Example of Implicit Measure:

In this example, Let's assume that I have a dataset containing sales data for a company. If I create a bar chart and drag the Sales field to the Value area, Power BI automatically creates a sum aggregation for the Sales field, which becomes an implicit measure.

Explicit Measures in Power BI

Explicit measures in Power BI are measures that are created by the user using DAX formulas. Explicit measures are highly customizable and can be used to create more complex calculations. Explicit measures are created using the New Measure option in the Modeling tab in Power BI.

Advantages of Explicit Measures:

• They provide more flexibility and functionality than implicit measures.
• They can be customized to meet specific business needs.
• They can be used to create more complex calculations, such as ratios or percentages.

Disadvantages of Explicit Measures:

• They require DAX formula writing, which can be challenging for some users.
• They may take more time and effort to create than implicit measures.
• They may require more maintenance and updates as the data model changes.

Example of Explicit Measure:

In this example, Let's assume that I have a dataset containing sales data for a company. I want to create a measure that calculates the sales growth from the previous month. To do this, I will create an explicit measure using the following DAX formula:

Sales Growth = 
IF ( 
    ISFILTERED (Sales[Date]), 
    ERROR ("Time intelligence calculations must use a continuous date column."),
    VAR CurrentSales = [Total Sales]
    VAR PreviousSales = CALCULATE ( [Total Sales], PREVIOUSDAY ( Sales[Date] ) ) 
    RETURN 
          DIVIDE (
                  CurrentSales - PreviousSales,
                  PreviousSales,
                  0
          )
 )         

Conclusion:

Both Implicit Measures and Explicit Measures are useful in creating visualizations in Power BI. Implicit measures are automatically created by Power BI and are quick and easy to use, while explicit measures provide more customization and flexibility, but require DAX formula writing skills. It is important to choose the right type of measure for the task at hand to ensure accurate and insightful visualizations.

Please share your thoughts and opinions in the comment section below. Also, feel free to share this article with your network to spread the knowledge.

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