Explicit vs. Implicit Measures in Power BI

Difference Between Explicit vs Implicit Measures in Power BI

Implicit Measures and Explicit Measures are used to create visualizations in Power BI. Explicit measures are user-defined calculations created using DAX while implicit measures are automatically generated based on the fields you drag and drop into a visualization. Explicit measures provide more control and customization than Implicit Measures. Explicit measures are more suited for achieving more sophisticated requirements in a semantic data model while Implicit measures are convenient for simple scenarios when you want to quickly create visualizations without manually defining measures. 

Let us compare the two in detail in the article below. Here are the critical differences between Explicit measures and implicit measures in Power BI:

Definition of Measures

  • Explicit measures are manually created by the user using DAX (Data Analysis Expressions). Users define the calculation logic and create a new measure in the Power BI Desktop using DAX functions.
  • Implicit measures are dynamically created by Power BI when dragging a field into a report visualization that requires an aggregation (e.g., sum, average). Power BI automatically performs the aggregation based on the data type of the field.

User Control

  • Users have complete control over the calculation logic and formula when they create Explicit Measures. They define the measure’s behavior and can create complex calculations tailored to their specific requirements.
  • In the case of Implicit Measures, Power BI automatically aggregates the data based on the field’s aggregation properties, and users have less direct control over the calculation logic. The visualization’s requirements determine the aggregation.


  • Explicit Measure provides flexibility as users can create custom calculations, apply conditional logic, and use a wide range of DAX functions to achieve the desired result.
  • Implicit Measure offers limited flexibility as Power BI determines the aggregation based on the data type in Power BI of the field and the requirements of the visualization. Users may need more ability to customize the calculation.

Measure Reuse

  • Users can reuse explicit measures in multiple visuals, dashboards, or reports, promoting consistency and efficiency.
  • Implicit measures can’t be reused as the aggregations are specific to the visualization where the field is used, and users may need to create additional explicit measures for more complex calculations or reuse calculations in other parts of the report.


  • By using Explicit measures, performance can be optimized through the use of efficient DAX formulas. Users have control over how calculations are performed, allowing them to optimize for performance.
  • In the case of Implicit measure, users have less direct control over the optimization of calculations.

What are Explicit measures in Power BI?

In Power BI, explicit measures refer to calculated measures that can be created explicitly using the DAX (Data Analysis Expressions) language. DAX is a formula language in Power BI and other Microsoft tools for creating custom calculations and aggregations in data models.

  • Report authors and designers specifically create explicit measures by using the DAX within Power BI and, once created, are saved with the semantic model.
  • Any user with edit access to the semantic model can create explicit measures in Power BI. These measures are available in the dataset as fields and can be used across calculations, reporting, or any other data analysis purposes.
  • If you drag and drop any explicit measures into the report canvas, Power BI doesn’t apply any aggregations to such measures.

Explicit measures allow you to create more complex and customized calculations based on your specific requirements. These measures can involve mathematical operations, conditional logic, and references to other columns or tables in your data model.

An example of Explicit measure in Power BI is highlighted below:

Total Valuation of Top 100 companies = SUMX(FILTER(Top_1000_Companies_Dataset, Top_1000_Companies_Dataset[GrowjoRanking] < 101), Top_1000_Companies_Dataset[valuation])

Explicit vs. Implicit Measures in Power BI - Example

Benefits of Explicit Measures

  • Explicit measures provide flexibility in defining the measures depending on the data model.
  • You can efficiently define the measures that can significantly improve data visualization and reporting.
  • It enables you to create complex calculations and customize measures for reporting requirements.
  • You can amend any existing explicit measures to alter the logic provided you have the edit access.


  • Explicit measures can be complex depending on the DAX formula used and hence require prior experience in DAX
  • You have to manually define the explicit measures using DAX or use the built-in measures in Power BI

Power BI provides flexible means of creating explicit measures for a data model. To create an Explicit measure in Power BI:

Step 1: Open your Power BI report.

Step 2: Go to the “Data” view.

Step 3: In the Fields pane, right-click on the table for which you want to create the measure.

Step 4: Choose “New measure” from the context menu.

Step 5: In the formula bar that appears, you can write your DAX expression to define the calculation for the measure.

Step 6: Once you create an explicit measure, you can use it in your visualizations, just like any other field or measure in your data model.

What are Implicit measures in Power BI?

Implicit measures are automatically created by Power BI when you drag and drop a field onto the values area of a visualization. Power BI automatically applies an aggregation to that field. Power BI will automatically aggregate the data based on the context of the visualization, and you don’t need to create a measure explicitly.

  • Implicit measures are convenient for quick visualizations, but they may only sometimes represent the exact calculation you need.
  • You should create explicit measures for more complex calculations. Users with edit access can create implicit measures in the Power BI report.
  • When you use an Implicit measure, you can start with a summarization technique and amend it as per your visual requirements.

Some of the aggregation functions in Power BI supported in Implicit measure include SUM, AVG, MIN, MAX, COUNT, STDDEV, etc., and can only be applied to numeric columns. However, you can define the other aggregations as per the different options available in the context menu highlighted below:

Explicit vs. Implicit Measures in Power BI - Aggregation

Benefits of Implicit Measures

  • Implicit measures are easy to use and learn.
  • You don’t require any prior understanding of DAX to define Implicit measures.
  • You can save time and effort as you don’t have to create the measures explicitly.


  • Implicit measures can’t be helpful in creating complex calculations or applying customization as per your data model.
  • It works for simple scenarios and applies auto aggregation with little or no flexibility to define your measures

An example of Implicit measure in Power BI is highlighted below:

If you drag and drop the “Sales” field from the “Country_Product_Sales” table onto the report canvas, Power BI will automatically sum the values to create an implicit measure for each segment.

Explicit vs. Implicit Measures in Power BI - Implicit Example

To create an Implicit measure in Power BI:

Step 1: Open your Power BI report.

Step 2: Go to the “Data” view.

Step 3: In the Fields pane, drag and drop the numeric fields into the report canvas. Power BI then automatically creates an implicit measure in the report canvas that you can use for your reporting purpose.

Step 3

Comparative Table

There are several similarities and differences between the Explicit measure and the Import measure. This section captures the key differences between these modes in a comparative table below:

CategoryExplicit measureImplicit measure
DefinitionManually created by the report author or designer by using the DAX formula or using the predefined measures feature, i.e., Quick measure in Power BI.Automatically created in Power BI as per the data model selection and numeric field chosen.
Aggregation mechanismPower BI users have to define the aggregation or other calculations as per the reporting requirements.Aggregation is automatically applied as per the data field in Power BI.
Complex calculationsAllows the user to define complex calculations to create measures as per their requirementsIt does not provide any scope for advanced or complex calculations.
Knowledge of DAX formulaeUsers need to have prior knowledge of DAX formulae to create explicit measures.No knowledge of the DAX formula is required, as users can drag and drop the fields to create the implicit measures.
CustomizationsUsers can apply customization to the explicit measures as per their requirements For example, Adding context filters, calculations, and other functionalities.Difficult to customize the implicit measures as they are created automatically and only aggregations are supported.
MaintenanceExplicit measures require ongoing maintenance and regular updates depending on data model changesNo additional maintenance is required for implicit measures.
Ease of UseExplicit measures can range from simple measure formulas to complex ones, depending on how it’s written.Implicit measures are easy to use as you have to simply drag and drop the fields.
FlexibilityHighly flexible, as users define custom calculations using DAX.Limited flexibility, as it relies on Power BI’s assumptions for aggregation.
User controlUsers have full control over the logic and formula of the measureLess control, as it relies on Power BI’s default summarization.
Multidimensional Expressions (MDX)Supports multidimensional expressionsThese measures don’t work if you use multidimensional expressions to query the data model.


Despite of differences between Explicit measures and Implicit measures, there are some similarities between these measures that can help you choose the right mode for your data visualization requirements.

Some of the key similarities are highlighted below:

Data Visualization

  • Both Explicit measures and Implicit measures are created to support data visualization requirements in Power BI.
  • Users can use these measures to create graphs, charts, or other visuals to present the data in the Power BI dashboard or reports to the end users


  • You can edit both Explicit measures and Implicit measures as per your data reporting requirements. Though Explicit measures provide a greater degree of customization, you can only change the aggregation method in the case of Implicit measures (For example: changing from SUM to AVG using the context menu)


  • You can view both Explicit measures and Implicit measures in the report canvas in Power BI.
  • Though Explicit measures are available in the Fields pane and can be used in other calculation references, you can’t reference Implicit measures in any of your calculations

Relationship and context

  • Both explicit and implicit measures are influenced by the relationships established between tables in the data model.
  • They both take into account the context of the data displayed in visuals, responding to filters, slicers, and other elements that define the scope of analysis.

In summary, explicit measures provide more control and flexibility to users for defining custom calculations using DAX, while implicit measures are automatically created by Power BI based on field aggregations and may have limitations in terms of customization. The choice between explicit and implicit measures depends on the specific requirements and complexity of the calculations needed in a Power BI project.

This has been a guide to Explicit vs. Implicit Measures in Power BI. Here we learn the benefits, limitation, category, with their differences & similarities. You can learn more from the following articles –

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