In Power BI, measures are computations applied to data to give values that support analysis or decision-making. Broadly, measures in Power BI can be categorized into two types: implicit and explicit.
Implicit Measures:
Implicit measures are created automatically by Power BI when you drag a field, typically a numerical one, into Power BI visuals, e.g., tables and charts. Power BI implicitly derives an aggregation such as SUM, COUNT, MAX, MIN, or AVG for these measures.
Let’s assume we have a Sales table with two columns, Item (string) and Amount (number). If we drag the Amount filed into a visual, Power BI automatically creates an implicit measure that sums up the amounts.
Explicit Measures:
Explicit measures, conversely, are user-defined and created using the DAX (Data Analysis Expressions) formula language. These measures offer a broader control over computation and allow for more complex calculations. For example, you might want to calculate the average sale per item or the percentage change in sales compared to the previous period. These calculations might be difficult or impossible to achieve with implicit measures.
You could create a DAX measure for the sales table like this:
SalesAvgPerItem = AVERAGEX ('Sales', 'Sales'[Amount])
This provides the average amount per item in our sales, which is more precise compared to an implicit measure.
Advantages of Replacing Implicit Measures with Explicit Measures
While implicit measures can be beneficial for quick and straightforward aggregations, explicit measures offer several advantages:
- Greater flexibility: Explicit measures offer a greater range of functions and allow users to write custom DAX expressions.
- Greater control over calculation logic: With explicit measures, users have the exact control over the calculation logic, which can ensure that the measures are calculated as intended.
- Improved performance: Explicit measures can often deliver improved performance, especially for big data, as they can be optimized in many ways than implicit measures can’t be.
Steps to Replace Implicit Measures with Explicit Measures
To replace an implicit measure with an explicit one in Microsoft Power BI, below are the steps:
- Identify the field which is used as an implicit measure.
- Evaluate what implicit aggregation it uses.
- Develop a corresponding DAX formula for that implicit measure.
- Create a new measure and paste your DAX formula.
- Replace the implicit measure in your visuals with your new explicit measure.
Following these steps will give you greater agility and precision in dealing with complex calculations which are essential for in-depth data analysis and visual reporting. Applying such knowledge in the PL-300 Microsoft Power BI Data Analyst exam serves to demonstrate proficiency in handling various data modeling scenarios, enhancing your skillset and contributing to comprehensive data analysis.
Practice Test
True or False: An implicit measure in Power BI can be identified by its calculations which are automatically created by a summarization query.
- True
- False
Answer: True
Explanation: An implicit measure in Power BI are those calculations that Power BI Desktop automatically creates when a field is added as a value to a visualization.
What are the common examples of implicit measures?
- Count
- Average
- Median
- All of the above
Answer: All of the above
Explanation: Average, Median, Count, etc., are some of the common implicit measures that Power BI Desktop will create when a field is added as a value to a visualization.
True or False: Converting implicit measures to explicit measures in Power BI improves flexibility and maintainability while developing reports.
- True
- False
Answer: True
Explanation: It is always recommended to use explicit measures as they offer improved flexibility and maintainability.
Which of the following is not an advantage of converting implicit measures to explicit measures?
- Provides more control over calculations
- Increases the flexibility and maintainability of reports
- Limits the calculation to current visualization only
- All calculations can be easily viewed in the fields pane
Answer: Limits the calculation to current visualization only
Explanation: This is not an advantage but a disadvantage. Explicit measures do not limit the calculation to the current visualization like implicit measures.
Which of the following statements about explicit measures is incorrect?
- They can be created by the data analyst
- They are flexible and maintainable
- They cannot be used in different visualizations
- They appear with a calculator icon next to them
Answer: They cannot be used in different visualizations
Explanation: Unlike implicit measures, explicit measures can be reused in different visualizations.
True or False: Power BI allows creating explicit measures through the calculation area.
- True
- False
Answer: True
Explanation: Power BI provides a calculation area for creating explicit measures.
True or False: Implicit measures are created by the data analyst manually.
- True
- False
Answer: False
Explanation: Implicit measures are not created manually; they are generated automatically when a field is added to a visualization.
In which pane do explicit measures appear in Power BI?
- Fields
- Visualization
- Data
- Review
Answer: Fields
Explanation: Explicit measures are listed in the Fields pane and distinguished by a calculator icon next to them.
Which function(s) can be used to replace implicit measures with explicit measures in Power BI?
- SUM
- COUNT
- AVERAGE
- All of the above
Answer: All of the above
Explanation: All these functions can be used while creating explicit measures to replace implicit ones depending on the requirement.
True or False: You cannot control the summarization behavior in explicit measures.
- True
- False
Answer: False
Explanation: Explicit measures provide more control over the summarization behavior than implicit measures.
Interview Questions
What is the difference between implicit and explicit measures in Microsoft Power BI?
Implicit measures in Power BI are auto-calculated aggregations of a column, such as a sum, count, or average. They are automatically created by Power BI. Explicit measures, on the other hand, are user-created calculations that use DAX formulas. These calculations are more customizable and can return various results based on the specific needs of the user.
Why might you need to replace an implicit measure with an explicit measure in Power BI?
Replacing an implicit measure with an explicit measure can provide more customization and control over the calculations. For example, with an explicit measure, a user could create a complex calculation with multiple steps and logical conditions. This would enable more complex analyses and insights compared to an implicit measure.
How do you create an explicit measure in Power BI?
To create an explicit measure in Power BI, you would go to the modeling tab, click on the “New Measure” button, and then write the DAX formula for the calculation you want to make. The new measure can then be used in reports.
What role does DAX play in creating explicit measures in Power BI?
DAX (Data Analysis Expressions) is the formula language in Power BI that enables the creation of explicit measures. DAX allows for creating simple to complex calculations which help in tailoring data analysis according to specific needs.
What is a key advantage of using explicit measures over implicit measures in Power BI?
The key advantage of using explicit measures is the level of control and customization they offer. Implicit measures are bound by predefined aggregation methods like sum, average, or count, whereas explicit measures, with the use of DAX, allow users to define their own sophisticated calculations.
How will replacing an implicit measure with an explicit measure affect a visualization?
The outcomes in visualization can vary depending on the complexity of the explicit measure. As explicit measures may provide deeper insights, the visualizations could potentially be more insightful and detailed.
Is there a performance implication between using implicit and explicit measures in Power BI?
Implicit measures might be slightly quicker to calculate as they are basic aggregations. However, explicit measures, while potentially more resource-intensive, provide the advantage of flexibility and customization that can lead to more insightful analyses and reports.
What is the role of context in explicit measures in Power BI?
Context is crucial in explicit measures as DAX formulas consider row context and filter context. This helps to deliver dynamic results depending on the conditions specified in the calculation.
What practical steps can be taken to replace implicit with explicit measures in Power BI?
To replace implicit measures, you must first identify them. Then, an equivalent DAX calculation must be formulated for that measure. This measure must be created using the “New Measure” button under the modeling tab, and then defined with its DAX formula.
Can both implicit and explicit measures be used in a Power BI report?
While both implicit and explicit measures can be used in a report, it’s usually recommended to use explicit measures because they offer greater flexibility and precision in the calculations.
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