Power BI Quick Measure: Boosting Efficiency and Insights

Power BI supports both implicit and explicit quick measures. Internal calculations that Power BI automatically generates are implicit quick measures, while complex calculations users create are typically represented as model and Power BI quick measures. These are usually easy to change and implement.

In this guide, you’ll discover what Power BI quick measure is, and why it is important. You’ll also discover how to create a quick measure.

Table of Contents:

1. Define a Quick Measure in Power BI
2. Why is Quick Measure in Power BI important?
3. Difference between Measures vs Quick Measure in Power BI
4. How to Create Power BI Quick Measures?
5. How to Use Quick Measure in Power BI?
6. How to Analyze Quick Measures in Power BI?
7. Wrap Up

First…

Define a Quick Measure in Power BI

Definition: Quick measures are used for performing calculations. They are made of Data Analysis Expressions (DAX) queries that run in the background, and the results are usually displayed in the report.

Therefore, writing DAX queries in Power BI is unnecessary when creating quick measures or calculated measures, as the calculations running in the background will automatically be displayed in the report.

Why is Quick Measure in Power BI important?

Quick Measure in Power BI is important for several reasons:

1. Ease of Use: With quick measures, you don’t need to have deep knowledge of DAX or be familiar with the DAX format before creating common calculations. It’s a good fit for users who cannot write complex DAX formulas.
2. Accuracy and Consistency: Quick measures take advantage of consistent and accurate calculation patterns. This reduces the chances of error as calculations adhere to best practices and standard methodologies.
3. Time Savings: With mere clicks, quick measures enable users to create powerful calculations. This eliminates the time spent on the manual creation of DAX expressions. It also helps report builders and analysts spend more time analyzing data and drawing insights from it.
4. Customization and Flexibility: Quick measures give room for data customization. Users can customize their DAX code to meet their desired requirements. It also enables them to tweak calculations in such a way that aligns with their business needs.
5. Standardization: Quick measures always come with standardized calculation templates. This ensures that there is consistency across dashboards and reports.

Difference between Measures vs Quick Measure in Power BI

Here are the major differences between Measures vs Quick measures in Power BI.

Measures:

• Definition: Power BI measures are typically written in DAX and they’re used for performing transformations, calculations, and aggregations on data within the Power BI model.
• Manual Creation: Measures are manually created and they’re written using DAX formulas. To get started, you need to have a good grasp of DAX syntax and functions.
• Flexibility and Control: Since measures are created from scratch, users get full control and flexibility over the calculations and logic. Users have the option of customizing measures to fit their business.

Quick Measures:

• Definition: Quick measures are predefined patterns or calculation templates offered by Power BI. Quick measures enable users to perform calculations without manually writing DAX codes.
• Template-based Creation: Users can choose calculation types and input parameters when working with quick measures. Power BI also generates the DAX formula that fits your chosen template.
• Ease of Use: Quick measures are simple and accessible and they’re a good fit for users who are not familiar with DAX. It gives users the option of creating complex calculations using a few clicks.

How to Create Power BI Quick Measures?

Here’s a brief overview of how to create quick measures.

1. Access Quick Measure: After opening Power BI Desktop, go to the “Modeling” tab.
2. Initiate New Quick Measure: Select “New Quick Measure” to get the Quick Measures dialog box opened.
3. Select Calculation Type: Choose your desired calculation type from a list of categories like Time Intelligence and Aggregation. Common examples include Year-to-Date Total, Average, and Sum.
4. Specify Calculation Inputs: From your calculation type, choose your preferred fields (or columns) from the data model. For instance, you can choose a date field for time-based calculations.
5. Configure Additional Parameters: There are instances where you’ll need additional parameters when working with quick measures. These could be base measures when working with percentage calculations.
6. Review and Name: Review your quick measure, and come up with a custom name (if needed).
7. Create the Quick Measure: To generate your quick measure, click “OK.” After that, a new measure will be automatically created by Power BI.
8. Utilize in Reports: The new quick measure is made available in the Power BI reports. To analyze data based on your calculated values, you’ll have to drag the quick measure onto the visuals and drop it. Visuals could be charts or tables.
9. Optional Customization: There is an option to customize the DAX formula. To do that, you’ll have to edit the quick measure in your fields pane.

How to Use Quick Measure in Power BI?

From the demonstration file in File Explorer, let’s create a quick measure for the average sale in each region. Navigate to the Reports window Quick Measure, and take the steps below.

1. Navigate to the Visualization pane, and click “Matrix.”
2. Expand your customer table, and drag the “Country-Regions” onto the Rows bucket.
3. Expand your Sales table, and drag the Sale Amount onto the Values bucket. That is seen in Figure A.

The quick measure will be added to the table by Power BI. You’ll have to select the Sales table at this stage. Since the matrix visualization has already been selected, you’ll need to click on the quick measure icon. The quick measure icon is found at the top-right side and it has a lightning bolt sign.

From the Calculation dropdown menu that shows up on the next page, choose Average Per Category. Next, expand the Sales table, and drag the Sales Amount onto the Base Value bucket. Click on the dropdown menu, and select Average (Figure B).

Figure B

Expand your customer table, and drag the Country-Region onto the Category bucket (Figure C). After that, click OK.

Figure C

From Figure D, you’ll notice that the new quick measure is added to the Sales table by Power BI. However, it won’t be displayed by Power BI until you add it.

Figure D

Before moving on, you should change the default name. Here’s how to do that:

• Right-click on the quick measure icon, and choose the rename option.
• Enter the new name. You can include the word “measure” during the naming.

Figure E below shows you how to add the new measure to the matrix visualization.

The new measure will most likely work in the other visualization. From Figure F, you’ll see that the quick measure updates align with any changes made. For instance, the quick measure updates to accommodate changes made when the Country-Region field is removed, and City is added. You can also shorten the name to “Average Sale.”

How to Analyze Quick Measures in Power BI?

Stage 1: Logging in to Power BI

• Log in to Power BI.
• Enter your email address and click the “Submit” button.

• You’ll be redirected to a Microsoft account. Enter the password and select the “Sign in” option.

• Choose to stay signed in.

• After that, the Power BI home screen will be displayed.

Stage 2: Create a Data Set and Select the Data Set to be used in the Sankey Chart.

• On the left-side menu, click on the “Create” button.
• Choose “Paste or manually enter data.”

• The sample data below will be used for this illustration.

Application Channels	Initial Screening	Conduct Interviews	Employee Onboarding	Total Candidates
Social Media	Short Listed	Final Interview	Hired	32
Social Media	Short Listed	Final Interview	Not Hired	400
Social Media	Short Listed	Knocked Out		800
Social Media	Knocked Out			1100
Company Career Page	Short Listed	Final Interview	Hired	20
Company Career Page	Short Listed	Final Interview	Not Hired	250
Company Career Page	Short Listed	Knocked Out		500
Company Career Page	Knocked Out			900
Events	Short Listed	Final Interview	Hired	5
Events	Short Listed	Final Interview	Not Hired	100
Events	Short Listed	Knocked Out		200
Events	Knocked Out			350
Paper Media	Short Listed	Final Interview	Hired	3
Paper Media	Short Listed	Final Interview	Not Hired	80
Paper Media	Short Listed	Knocked Out		135
Paper Media	Knocked Out			700
Employee Referrals	Short Listed	Final Interview	Hired	10
Employee Referrals	Short Listed	Final Interview	Not Hired	70
Employee Referrals	Short Listed	Knocked Out		80
Employee Referrals	Knocked Out			110
Direct	Short Listed	Final Interview	Hired	25
Direct	Short Listed	Final Interview	Not Hired	150
Direct	Short Listed	Knocked Out		425
Direct	Knocked Out			600

• Navigate to the “Power Query” window and paste the data table above. From the list that comes up, choose “Create a dataset only.”

• From the left-side menu, select the “Data Hub” option. If there’s a data set list, it will get populated by Power BI. However, if there is no data set list, an Error message will be displayed.

• From the dropdown menu, click on “Create report.”

• If you’re using ChartExpo, you can add the Power BI Sankey Diagram Extension by following the steps below.
  • You need to have the add-in or get the Power BI visual from AppSource
  • The Power BI visualization panel is found on the right side of the Power BI dashboard
  • Click on the ellipsis symbol (…)
  • From the menu that comes up, choose “Get more visuals.”

• In the next window that opens up, input “ChartExpo” into the search box. The “Sankey Diagram for Power BI by ChartExpo” will be displayed, click on it.

• Click on the “Add” button.

• The “Sankey Diagram for Power BI by ChartExpo” icon will be added to the visualization panel.

• To draw a Sankey Diagram with ChartExpo Power BI extension, you’ll have to:
  • Choose the “Sankey Diagram for Power BI by ChartExpo” option from the visualization panel.
• After that, a window similar to the one below will be displayed on your screen.

• Choose the fields you’ll use from the given options.

• When selecting the fields, follow the sequence below:
  • Application Channels
  • Initial Screening
  • Conduct Interviews
  • Employee Onboarding
  • Total Candidates.
• You’ll have to provide your email address or ChartExpo license key. You’ll see a space where you’ll input the license key.

• Your chart will be displayed after you’ve inputted the key. Navigate to the General section and add the top header under it.

• Click on the visual to set your number and enable the options as shown below.

• The image below shows you how to change the “Node Font Style.”

• Here’s how to change the “Level Font Style.”

• The image below shows you the Level Labels.

• The image below shows you how to change the Node’s color.

• From the image above, you’ll see “How to change Level 1 color.” The image below shows you how to change the node color of other levels.

• After changing the color of all nodes, here’s what the HR dashboard looks like.

Insights

• The application channel that has the highest number of candidates (1100) is social media.
• 600 candidates applied directly
• Social media has the highest number of shortlisted candidates (32 hired, 400 not hired).
• Paper Media and Events have the lowest number of shortlisted candidates.

FAQs

How do I edit a quick measure in Power BI?

You’ll need to right-click on the quick measure in the field plane. After that, choose the “Edit” option to modify the DAX formula.

What time intelligence calculations are supported by quick measures?

Time intelligence calculations supported by the quick measure are the same Period Last Year, Moving Total, Previous Period Total, and Year-to-Date Total.

Wrap Up

Quick measure in Power BI is a calculation mode that enables the user to create quick measures without the need to write DAX code. It enables faster development of reports and eases the creation process for non-experts. It also helps to ascertain that calculations across multiple reports are consistent.

Creating an attractive dashboard in Power BI involves the use of a Sankey chart, and also similar visualizations. This visualization helps to effectively evaluate data.

Power BI quick measure can serve as an educational resource for users who are learning DAX. With Power BI quick measure, users can easily take a close look at generated DAX code and figure out the underlying logic.

Now you know how Power BI quick measure works, what data will you be representing with the tool?