Power BI Summarize Making Charts More Insightful

The Power BI Summarize DAX function is a tool designed for data analysis and aggregation. Users can use the tool to create summaries, group data, and calculate totals based on specified columns.

In this guide, you’ll discover what the Power BI summarize function is, why the summarize function in Power BI is important, and what is the use of summarize Power BI function.

Table of Contents:

1. Define Power BI Summarize Function
2. Understanding the syntax of the SUMMARIZE DAX Function
3. Why is the Summarize Function in Power BI Important?
4. What is the Use of the Summarize Power BI Function?
5. How to Use Power BI Summarize DAX Function?
6. How to Visualize Your Data in Power BI?
7. Wrap Up

First…

Define Power BI Summarize Function

The Power BI SUMMARIZE DAX function enables users to create summary tables from existing data tables. If the columns to group the data by is defined, users can generate aggregated values and calculate subtotal and total values based on the grouped columns. The result is usually a more concise and organized representation of the data, making analyzing and interpreting data easier and more efficient. This streamlined approach enhances clarity and decision-making.

Understanding the syntax of the SUMMARIZE DAX Function

To effectively use the SUMMARIZE DAX function, you have to understand its syntax. The function takes two main arguments: the table name and a list of column expressions.

The table name shows the data table from which you want to summarize the data, while the column expressions show the grouping columns and any additional expressions (or calculations) you want to include in the summary table.

The column expressions list could include calculated columns, simple column references, or measures. If you combine these expressions with appropriate grouping columns, you’ll create flexible and powerful summary tables that meet your specific analysis requirements.

Here’s the basic syntax of the SUMMARIZE function in Power BI:

• SUMMARIZE (

<table>,

<grouping_column1>, [<grouping_column2>, …],

“<new_column1>”, <expression1>,

“<new_column2>”, <ex[ression2>,

…

)

Why is the Summarize Function in Power BI Important?

• Aggregation and summarization: Users can aggregate and summarize data from a large dataset into a more manageable form, enabling effective data analysis. This approach provides insights at different levels of granularity, playing a crucial role in reporting and analyzing large data volumes.
• Performance optimization: Summarizing data using SUMMARIZE helps Power BI to optimize query performance. And that comes in handy when you’re dealing with Power BI datasets or complex calculations. Aggregating data at the database level also helps in reducing the data volume being processed and transferred. This will, in turn, lead to faster query execution times.
• Improved visualization: Summarized data is easy to interpret and visualize when compared to raw data. By summarizing data with SUMMARIZE, users will create more insightful and meaningful data visualizations that help stakeholders understand patterns, outliers, and trends in the data.
• Customized summaries: SUMMARIZE helps users create customized summary tables that are tailored to their analytical needs. The user can easily define which columns to include in the summary table, how to group the data, and which aggregate functions to apply to each group.

What is the Use of the Summarize Power BI Function?

• Data Aggregation: Users have the option to aggregate data from multiple tables based on specified grouping columns. This plays a vital role in creating summary tables that show aggregated values like averages, totals, or counts.
• Performance Optimization: By summarizing data at the database level, SUMMARIZE helps in optimizing query performance. That comes in handy when you’re dealing with large datasets. Aggregating data lowers the data volume processed and transferred, and that leads to faster query execution times.
• Creating Summary Tables: The function creates summary tables with aggregated data, which makes it easier to visualize and understand patterns, trends, and outliers in the data.
• Grouping Data: SUMMARIZE groups data based on the unique combinations of values in the specified grouping columns. This will, in turn, help in organizing data into meaningful groups for reporting and analysis.
• Supports Data Modeling: The SUMMARIZE function supports the creation of calculated tables, measures, and calculated columns. That enables users to build sophisticated data models that support complex analytical requirements.

How to Use Power BI Summarize DAX Function?

• The data table below will be used to demonstrate the SUMMARIZE DAX function.

• Upload the data table to the Power BI desktop file.

You should remember that the “SUMMARIZE” function groups the data in a new table. You can apply the SUMMARIZE function only when you create a new table. This way, it cannot be used as a “NEW COLUMN” or “NEW MEASURE.”

• From the data table above, you’ll have to summarize the data based on “State.” To do that, navigate to the “Modeling” tab and choose the “New Table” option.

• You will have to name the table first. You can name it “State Summary Table.”

• Open the Power BI SUMMARIZE function.

• You have to mention the “Table” you’re trying to summarize. In this case, the table you’re summarizing is “Sales_Table,” and that doubles as the table name.

• You’re trying to summarize this table based on the “State” column. Therefore, this will be your “GroupBy_ColumnName1.”

• Once the column name is specified to summarize, you’ll need to give a name to the new column, “Sales Value.”

• “Expression 1” is nothing, but you need to summarize the mentioned column since you add sales values by state name. Open the SUM function to add sales column values by each state.

• The column name you need to SUM is the “Sales” column, so mention the same.

• Close the brackets and press the “Enter” key to get the summarized table based on state names.

How to Visualize Your Data in Power BI?

Stage 1: Log into Power BI, enter your email, and click “Submit.”

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

• You are redirected to your Microsoft account.
• Enter your password and click “Sign in“.

• You can choose whether to stay signed in.

• Once done, the Power BI home screen will open.

Stage 2: Create a Data Set and Select the Data Set to Use in the Sankey Chart

• Go to the left-side menu and click the “Create” button.
• Select “Paste or manually enter data“.

• We’ll use the sample data below for this example.

Quarters	Vendors	Market Share
Q1	Samsung	27.69
Q1	Apple	28.45
Q1	Xiaomi	11.8
Q1	Huawei	6.53
Q1	Oppo	5.3
Q1	Vivo	4.19
Q2	Samsung	28.14
Q2	Apple	27.58
Q2	Xiaomi	12.62
Q2	Huawei	6.17
Q2	Oppo	5.5
Q2	Vivo	4.21
Q3	Samsung	28.45
Q3	Apple	27.71
Q3	Xiaomi	12.9
Q3	Huawei	6
Q3	Oppo	5.29
Q3	Vivo	4.17
Q4	Samsung	27.97
Q4	Apple	27.62
Q4	Xiaomi	12.68
Q4	Huawei	5.17
Q4	Oppo	6.07
Q4	Vivo	4.66

• Paste the data table above into the “Power Query” window and name it “Market Share Analysis.” After that, select the “Create a dataset only” option.

• Navigate to the left-side menu, and click on the “Data Hub” option. Power BI will populate the data set list. If no data set has been created, you’ll get an error message.

• Click on “Create report.”

• Click on Market Share Analysis.

• Click on “Expand All.”

• To see the chart metrics, click on “Expand All.”

• Click on “Get more visuals.”

• Search ChartExpo and select Comparison Bar Chart.

• Click on “Add.”

• After that, you’ll see the Comparison Bar Chart in the visuals list.

• You can expand the chart space.

• After that, you can select the metrics of your data.

• You can click on the second icon of Format Visuals and click on Visuals.

• In Visual, click on License Settings and add the key. After adding the key, you will see the comparison bar chart.

• Click on the General tab to add the header text. Add the header text in the Title.

• Here’s the final look at the Comparison Bar Chart in Power BI.

Insights:

• Apple and Samsung are the top two vendors in terms of market share, and Samsung takes the lead.
• Xiaomi has been gaining market share, while Huawei has been losing market share.
• Vivo and Oppo have been relatively stable in terms of market share. In the last quarter, there was a transition between Huawei and Oppo.

Unlock Key Insights with Power BI Summarize in Your Charts:

1. Open your Power BI Desktop or Web.
2. From the Power BI Visualizations pane, expand three dots at the bottom and select “Get more visuals”.
3. Search for “Comparison Bar Chart by ChartExpo” on the AppSource.
4. Add the custom visual.
5. Select your data and configure the chart settings to create the chart.
6. Customize your chart properties to add header, axis, legends, and other required information.
7. Share the chart with your audience.

The following video will help you create a Comparison Bar Chart in Microsoft Power BI.

FAQs

What does summarize in DAX do?

SUMMARIZE in DAX groups and aggregates data from a table, and that creates a summary table with specified grouping columns and calculated expressions for reporting and analysis.

What is the alternative to summarize in DAX?

An alternative to SUMMERIZE in DAX is the GROUPBY function. The GROUPBY function aggregates and groups data from a table, and it offers similar functionalities for analysis and summarization.

Can you use summarize in DAX measure?

No, SUMMARIZE cannot be used directly in DAX measures. It is usually used to create calculated columns or tables, or within calculated table expressions (not measures).

Wrap Up

The Power BI SUMMARIZE function aggregates and groups data. It also helps in creating summary tables for analysis. The SUMMARIZE function is a good fit for users who are looking to efficiently organize and analyze large datasets, and also generate insights for decision-making.

The SUMMARIZE is a major function in Data Analysis Expressions (DAX), and the formula language is used in Power BI for creating measures, calculated tables, and calculated columns. With SUMMARIZE, users will be able to build sophisticated data models that support complex analytical requirements.

When it comes to creating summary tables, the function makes it easier to understand and visualize patterns, trends, and outliers in the data. SUMMARIZE helps in optimizing query performance, and that comes in handy for users who will be handling large datasets.

Now you know what the Power BI SUMMARIZE function is, how will you integrate it into your data analysis process?