This Power BI tutorial is intended to be hands-on. To follow along, download the Invoice Data from HERE and Customer Master Data from HERE. But first things first.

What is Power BI?

Before we tackle how to use Power BI, we should understand what it is. It’s a business intelligence (BI) tool from Microsoft. It allows you to import and model data from different sources, then analyze and visualize it, creating meaningful reports which you can securely share throughout your organization. It helps with informed decision-making, tracking key performance indicators (KPIs), identifying trends in the data.

It’s a must-know tool for any aspiring or seasoned data analyst.

Different Power BI apps

Power BI is actually a suite of products and services that each serve a different purpose. The main ones are:

  • Power BI Desktop – a desktop application available as a free download; this is where you build the report.
  • Power BI Service – an online portal for storing and sharing reports and dashboards. You can create a free account as long as you have a work or school email address, but the sharing capabilities only come with a paid license.
  • Power BI (mobile) app – a free app for viewing the published reports and dashboards, available for Windows, Android and iOS.

You may also come across:

  • Power BI Report Builder – a tool for authoring paginated reports (optimized for printing and PDF).
  • Power BI Report Server – an on-premises alternative to Power BI Service. The published reports are stored on a local server and shared internally via a secure portal.  

Power BI Desktop is the tool you’ll be using the most.

Installing Power BI Desktop

If you’re running Windows 10/11, Power BI Desktop can be installed for free from the Microsoft Store.

Fragment of the page for Power BI Desktop in the Microsoft Store with Install button visible.
Power BI Desktop in the Microsoft Store.

Just click the “Install” button and let Windows work its magic. The main advantage of this method is that it comes with automatic updates. Microsoft releases updates to Power BI monthly; this way you don’t need to reinstall every month.

If you are running an operating system other than Windows 10/11 (or a 32-bit version of Windows 10), you can download the Power BI standalone installer from the PowerBI.com website.

Fragment of the Power BI website with download button for Microsoft Power BI Desktop.
Microsoft Power BI Desktop download from the official website.

Click the “Download” link to download and install Power BI Desktop.

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Launching Power BI

Once Power BI has been downloaded and installed, launch Power BI and sign in with your work or school account.

NOTE: You can also work in Power BI Desktop without signing in, but you will have to sign in once you’re ready to publish the report to the Service.

Power BI Welcome screen divided into 3 sections, with "Get data" & "Recent sources" on the left, "Collaborate and share" and "Get started" in the middle, and useful links on the right: "What's new", "Power BI blog", "Forums".
Power BI Desktop welcome screen.

The Power BI Main Components

Power BI Desktop is made up of three main components:

  • The Report View
  • The Data View
  • The Model View
Fragment of Power BI Desktop with Report View, Data View and Model View icons described.
Main views in Power BI Desktop.

The Data View displays tables of data acquired from source(s).

A table in Power BI Desktop Data view.
A table in the Data view.

The Model View displays the tables from the Data View in a bird’s-eye view showing the relationship connectors between the tables.

A diagram of a data model showing table names and fields in those tables, connected with lines denoting relationships.
A data model diagram in the Model view.

The Report View is where all visualizations are built and presented.

Power BI report with multiple charts and cards.
A sample Power BI report in Report View.

These visualizations can be tables, charts, graphs, slicers, KPIs, cards, buttons, logos, icons, etc.

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Importing and Transforming Data

Data sources for our report.

The first step in any report is to acquire the data that will drive the report visualizations.

This data can come from a variety of sources:

  • Delimited Text Files
  • Excel Workbooks
  • Databases
  • Websites
  • PDF Files
  • Folders
  • Online Services

The list of connectors is quite extensive and growing every month with regularly scheduled updates.

Clicking on Get Data provides a list of connectors.

Fragment of the Power BI Desktop welcome screen.
Get Data on the welcome screen.
Fragment of a list of data sources in Power BI.
Data Sources in Power BI (incomplete list).

For our demonstration, we will be pulling data from two different data sources:

  • An Excel Workbook named “CustomerMaster.xlsx” that contains a table named “MasterCustomer”. This table contains information about our customers, like name, contact information, location, etc.
  • A comma-delimited text file named “InvoiceData.txt” that holds invoice data (i.e., sales transactions.)

Loading the Invoice Data

Starting with the “InvoiceData.txt” file, we click Get Data then browse out to the text file.

We’re presented with a preview of the file’s content so we can verify that we have selected the correct file and that the proper delimiter is being used to parse the data.

Power BI window parsing InvoiceData.txt. With File Origin set to 1252 and Delimiter - Comma. Preview of the parsed table. Buttons: Load, Transform Data, Cancel.
Importing a text file to Power BI.

We can click Load to load the data directly into Power BI’s Data Model, or you can click Transform Data to open Power Query and perform any needed transformations on the data in preparation for loading to the Data Model.

Even if you don’t think the data requires any transformations, it’s a good idea to load it into Power Query first just to ensure that the data is being interpreted correctly (e.g., dates are being read as dates and not text or numbers.)

Power Query can be used to remove unwanted columns, filter unwanted rows, and even enhance the data by creating calculations, such as calculating the Profit from the difference between Sale Price and Cost.

InvoiceData open in the Power Query Editor, Queries pane on the right, table preview in the middle, and Query Settings pane on the left showing Properties (query name) and Applied Steps.
InvoiceData in the Power Query Editor.

If you have experience using Power Query in Excel, those experiences are transferrable to Power BI.  The program is 99% the same in both Excel and Power BI.

Applied Steps

Power Query performed several transformations steps automatically. These steps are listed on the right side of the window, as you can see in the above image.

  1. Source – the sales data was pulled from the source file.
  2. Promoted Headers – the first row in the data was promoted to a Header Row status.
  3. Changed Type – all columns were examined and assigned appropriate data types.

Although Power BI is good at performing certain transformations – like detecting data types – automatically, it’s a good idea to glance over the results to verify the actions are within your expectations.

Sometimes it’s not that an incorrect data type is assigned, but rather one “not as efficient”, e.g. a decimal data type assigned to a column of whole numbers.  The decimal data type would work, but if there’s no need to store fractions, a whole number data type will utilize memory more efficiently.

Merging Columns

We could also perform operations like combining columns.  In our text file, the “Year”, “Month”, and “Day” data is stored in separate columns.

We would like that information to be combined and transformed into a single “Date” column.

Fragment of a table in Power Query editor with arrows pointing to 3 headers: Year, Month, Day.
Separate Year, Month and Day columns.

While holding down Ctrl key, select the columns “Month”, “Day”, and “Year” in that order, then click Transform -> Merge Columns.

Location of the Merge Columns feature on the Transform tab of the Power Query Editor.
Location of the Merge Columns feature on the Transform tab.

We can define a Custom separator as a “/” (forward slash) and name the newly merged column “Date”.

Merge Columns window in Power Query Editor with a custom separator (forward slash) and new column name "Date".
Defining the separator in the Merge Columns window.

The newly merged column appears as follows:

Merged Date column in the Power Query Editor with Text data type.
Merged Date column.

The data type is not set correctly; see the “ABC” icon in the upper left corner of the column?

We can set this column to a “Date” data type by clicking the “ABC” icon and selecting “Date”.

Changing the data type of the "Date" column from Text to Date data type in the Power Query Editor.
Changing the data type in the Power Query Editor.

Notice that all the performed steps are listed in the Applied Steps panel on the right.

Applied Steps in the Power Query Editor.
Applied Steps in the Power Query Editor.

If someone updates the source text file, we will be able to click a button and refresh the data.  All of the recorded steps will be executed on the new data to update our Data Model.

Loading the Customer Information

The customer information is located in the Excel file named “CustomerMaster.xlsx”.

Bring the new data in by selecting Home (tab) -> New Source -> Excel Workbook.

Location of New Source on the Home tab of the Power Query Editor, with Excel Workbook selected.
Location of New Source on the Home tab of the Power Query Editor.

In the preview window, select the table named “MasterCustomer1” and click OK.

Power BI Navigator window showing preview of the tables and sheets in the imported workbook. MasterCustomer1 table selected.
Power Query Navigator window.

PRO TIP: If given the choice between selecting a sheet and selecting a table, it’s considered a best practice to select the table since it will confine the selection to the data range, and it will detect the header row if present.

MasterCustomer1 query in the Power Query Editor.
MasterCustomer query in the Power Query Editor.

Splitting Columns

A transformation we can apply to the customer data is to split the information in the “CityProvince” column into separate columns: one column for “City” and another column for “Province”.

CityProvince column in the Power Query Editor.
CityProvince column.

We can see that there is a space & open parenthesis between the city and the province.

Select the “CityProvince” column then click Transform (tab) -> Split Column -> By Delimiter.

Location of the Split Column feature on the Transform tab of the Power Query Editor. Split Column expanded and By Delimiter hightlighted.
Location of the Split Column feature on the Transform tab.

Set the delimiter to a space & open parenthesis ” (” and click OK.

Defining the custom delimiter (space and open parenthesis) in the Split Column by Delimiter window of the Power Query Editor.
Defining the custom delimiter in the Split Column by Delimiter window.

We now have the cities and the provinces in separate columns.

2 columns in Power Query Editor: CityProvince.1 with cities, and CityProvince.2 with states followed by a closed parenthesis.
CityProvince column split into two columns.

One small issue is the leftover closed parenthesis at the end of the province names.

We will remove them by replacing all “)” characters with nothing.

Select the “CityProvince.2” column and click Transform -> Replace Values.

Location of the Replace Values feature on the Transform tab of the Power Query Editor.
Location of the Replace Values feature on the Transform tab.

In the Replace Values dialog box, enter a closing parenthesis in the “Value to Find” field and leave the “Replace With” field empty.

Replace Values window in Power Query Editor, where "Value to Find" is populated with ")" (closing parenthesis), and "Replace With" left empty.
Populating the Replace Values parameters.

Now with perfectly separated cities and provinces, rename the columns “City” and “Province” respectively (simply double-click on the column header to rename).

2 columns in Power Query Editor: City with cities, and Province with states.
Separate and renamed City and Province columns.

Remove unnecessary columns

Lastly, we’ll remove the unneeded columns from the table by selecting Home -> Choose Columns.

Location of Choose Columns feature on the Home tab of Power Query Editor.
Location of Choose Columns feature on the Home tab.

Uncheck the unwanted columns and click OK.

Choose Columns window with a list of column names and several columns unchecked.
Choose Columns window.

Removing columns that do not contribute to the visualizations or aid in underlying functionality is a great way to reduce the size and complexity of the Data Model while simultaneously improving performance.

As with the invoice data, all the transformation steps for the customer data have been recorded and will execute automatically when the data is refreshed.

Applied Steps in the Power Query Editor.
Applied Steps in the Power Query Editor.

Final steps

Rename the query “MasterCustomer” using Properties – Name on the Query Settings pane.

Fragment of the Query Settings pane in power Query editor, showing Properties - Name: "MasterCustomer".
Renamed query.

We are ready to load the results of the Power Query transformations into the Data Model.

Click the button labeled “Close & Apply” on the Home ribbon.

Location of Close & Apply on the Home tab of Power Query Editor.
Location of Close & Apply on the Home tab.

After the data is loaded into the Data Model, switch to the Data View and examine the results of the query output.

Power BI Data view with InvoiceData and MasterCustomer tables loaded.
Power BI Data view.

Expanding the two titles on the right in the Data (previously Fields) pane shows all the loaded columns for each table.

InvoiceData and MasterCustomer tables expanded on the Fields pane showing all the column names for each table.
Tables expanded on the Data (previously Fields) pane.

Editing the Data Transformations

If you need to return to the Power Query Editor to modify the original transformations, right-click the query name on the Data (previously Fields) pane and select Edit Query.

Edit query option highlighted in the right-click menu of the Fields pane.
Edit query option in the right-click menu of the Data (previously Fields) pane.

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Exploring the Data Model

Switching to the Model View, we can see that the two tables have been connected using a relationship.

InvoiceData table and MasterCustomer table in Model view of Power BI, connected with a relationship between CustomerCode and CustomerID columns.
Relationship between tables in Model view.

The relationship has been built between the “InvoiceData – CustomerCode” column and the “MasterCustomer – CustomerID” column.

The table relationship allows us to perform real-time lookups of information from one table to another without having to merge the two tables into a single, giant table.

The “InvoiceData” table serves as the fact table (the table of things that happened), while the “MasterCustomer” table serves as a lookup table to enhance the facts.

This would allow us to take a [CustomerCode] and discover the [City] that the customer belongs to.

Creating Relationships Automatically

Power BI has a setting that permits automatic creation of relationships.  This setting can be activated or deactivated by selecting File -> Options & Settings-> Options -> Data Load -> Relationships.

Power BI Options window with Current File - Data Load opened and Relationships options highlighted. "Import relationships from data sources on first load" and "Autodetect new relationships after data is loaded" are checked.
Power BI Options window.

If the relationships are not created automatically (this can happen when working with unstructured sources with dissimilar field names), you can easily create them by dragging a field from one table and dropping it atop another field in a different table.

Building a Report

Reports are created in the Report View.

Fragment of the Power BI canvas in the Report view.
Power BI canvas in the Report view.

Creating visuals in Power BI is simple.  Check the boxes to the left of the fields of interest and let Power BI do the rest.

Field list in Power BI with Sales column in the InvoiceData table and CustomerCategoryName column in the MasterCustomer table checked.
Creating a visual by checking the fields.
Clustered column chart showing Sales by CustomerCategoryName.
Clustered column chart showing Sales by Customer Category.

Power BI has determined that the best chart for this data set is a Column Chart.  This can be changed to any other chart in the library by clicking the desired chart icon in the Visualizations Library.

Visualizations library in Power BI.
Visualizations library in Power BI.

Filtering the Data

We’ve switched the original Column Chart for a Bar Chart.

Clustered bar chart showing Sales by CustomerCategoryName.
Clustered bar chart showing Sales by Customer Category.

This chart is examining sales for all years.  Suppose we need to report sales by specific years.

Just like in Excel, we can add a Slicer visualization to easily filter between various years.

BEWARE:  If you click an icon in the Visualizations Library while an existing visual is selected, you will change the existing visual.  To create a new visual, ensure no existing visual is selected on the report.

To add Slicer visual to the report, click the Slicer icon in the Visualizations Library.

Visualizations library in Power BI with the slicer icon highlighted.
Slicer icon in the Visualizations library.

With the Slicer placeholder selected, click the [Year] field from the InvoiceData – Date – Date Hierarchy.

The Year field selected in the Date hierarchy of the InvoiceData table.
Select Year in the Date Hierarchy.

Power BI automatically created the Date Hierarchy to aid in the reporting of data by common periods (e.g., quarters, or months.)

Power BI Slicer in the Between style, with textboxes for years and a slider to move along the timeline.
Between slicer.

NOTE: If you are not getting the automatic Date Hierarchy when working with tables that contain date-based information, the “Auto Date/Time” may be disabled.  To enable the feature, click File -> Options & Settings -> Options -> Data Load -> Time Intelligence -> Auto Date/Time.

Fragment of the Power BI Options window with Time intelligence setting highlighted and Auto date/time checked.
Auto date/time in Power BI Options.

If you aren’t a fan of the Timeline version of the Slicer, you can change the Slicer Style on the Format pane. Select the slicer visual and go to Format -> Slicer settings -> Options -> Style.

NOTE: The slicer type setting has moved since the video was recorded. You will now find all the settings in the Format Options.

Slicer style settings on the Format visual pane, with Vertical list, Tile and Dropdown highlighted.
Slicer style settings on the Format visual pane.

Setting the Slicer to a Vertical List style yields the following:

Vertical List style slicer showing Years 2018, 2019, 2020, 2021 next to checkboxes.
Vertical List slicer.

Selecting a Dropdown style yields the following:

Dropdown style slicer in the collapsed and expanded state.
Dropdown slicer.

And Tile style yields clickable buttons.

Tile style slicer with year 2021 selected (unselected tiles have white background and the selected tyle has black background).
Tile slicer with year 2021 selected.

Formatting the Visuals

To format almost any visual aspect of the visual (e.g., font size, border, shadow, colors, labels, etc.) click the Paintbrush icon on the Visualizations pane.

Arrow pointing to the "Format visual" icon on the Visualizations pane in Power BI Desktop.
Format visual on the Visualizations pane.

NOTE:  Microsoft has redesigned the interface of Power BI Desktop since the video was recorded, with many esthetic changes affecting the Format visual interface.  The version of Power BI you are using likely differs from what you see in the video. The basic behaviors should be comparable, though.

Power BI Visualizations pane open on Format Visual, with X-axis expanded to show Values (expanded and toggled on), Title (collapsed and toggled off), and Layout (collapsed).
Customizable options after expanding chart element.

The available options will differ based on the type of visual you’re formatting. Expanding a feature will reveal the customization controls for that feature. The On/Off toggles turn features completely on or off.

In our project, we will turn off the titles for both the Y and X axis and change the color of the bars to orange.

Bar chart with orange bars titled "Sales by Category".
Formatted bar chart.

We also changed the title of our Bar Chart to “Sales by Category” to make the title more meaningful. You will find the Title under General formatting properties (i.e., not visual-specific, common to all visual types).

Format visual - General with Title expanded and title edited to "Sales by Category" and an arrow pointing from the title text box to a bar chart with the same title.
Editing the title of a visual.

Tooltips

Power BI is equipped with popup Tooltips.  These appear when you hover the mouse pointer over a visual’s data element.

Bar chart with a tooltip showing details of a datapoint.
Default Power BI tooltip.

Tooltips provide detailed information such as category name and aggregation value. They can be highly customized to even display mini charts which are filtered by the datapoint over which you hover.

Bar chart with a custom tooltip showing a filled map chart.
Custom tooltip showing a filled map chart.

Building a Table of Calculations

Now we’ll build a table that calculates the following:

  • Sales by Month
  • Year-Over-Year Percent Change
  • Sales Year-to-Date

To do this, we will utilize Power BI’s Time Intelligence capabilities without writing a single formula.

Add a Table visual to the report.

Pointing to the table icon the the Visualizations pane.
Table visual in the Visualizations library.

Place a check next to the [Month] field in the Date Hierarchy and the [Sales] field of the “InvoiceData” table. Notice the Sales values have been summarized automatically, like they would be in an Excel pivot table.

Table visual showing Months and summarized Sales for each month, with a Total on the bottom.
Table showing Sales by Month.

Comparing Sales of Same Month to Previous Year

If we are slicing (i.e., filtering) for the year 2019, to display the percentage change in sales for each month against the previous year, we can create this calculation using the Quick Measure feature located on the Home ribbon.

Quick measure feature on the Power BI ribbon in the Calculations group.
Quick measure.

Populate the Quick Measures dialog box as follows:

  • Calculation = Year-over-year change
  • Base Value = Sum of Sales
  • Date = Date
Quick measures window with Year-over-year change selected as the Calculation, Sum of Sales as Base value, and Date as Date.
Quick measures template for Year-over-year change.
Table showing Months and Sum of Sales for each month, and Sales YoY% for each month.
Table showing Total Sales and Sales Year-over-Year Percentage Change by month.

Calculating Sales Year-to-Date

To display the total sales year to date, we can create this calculation using the same Quick Measure feature located on the Home ribbon.

Populate the Quick Measures dialog box as follows:

  • Calculation = Year-to-date total
  • Base Value = Sum of Sales
  • Date = Date
Quick measures window with Year-to-date total selected as the Calculation, Sum of Sales as Base value, and Date as Date.
Quick measures template for Year-to-date total.
Table showing Months and Sum of Sales, Sales YoY%, and Sales YTD for each month.
Table showing Total Sales and Sales YoY% and Sales YTD by month.

Data Analysis Expressions (DAX)

What you’ve just done, without realizing, is written DAX measures. DAX, or Data Analysis Expressions, is the formula language of Power BI (as well as Excel’s Power Pivot). It underpins every calculation used in data visualizations, even if it happens “under the hood”, whenever you add a value field to a visual.

Or when you use Quick measures, which are ready-made formula templates that you just plug your data to.

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Adding More Visuals and Exploring Interactions

Visualizing Sales by Province

Our next visual will be a Filled Map chart.

Pointing to the Filled Map icon in the Visualizations library.
Filled Map in the Visualizations library.

Select [Sales] from the “InvoiceData” table and [Province] from the “MasterCustomer” table.

Map of North America showing Sales by Province (provinces with sales are filled in).
Filled Map showing Sales by Province.

(Most) Everything is a Filter

One of the great things about Power BI visuals is that almost every visual serves double-duty as a filter.  Slicers are not the only way to filter data in Power BI.

If you were to click on the bar for “Supermarket” in the Bar chart, all of the calculations in the table will change to reflect “Supermarket” values.  The Filled Map chart will also change to show only states where “Supermarket” sales occurred. This is called cross-filtering.

Power BI report page filtered to year 2019 by a dropdown slicer, with 3 visuals: a bar chart, table, and map chart, all filtered by the selection of the Supermarket category made in the bar chart.
Power BI report cross-filtered by Supermarket Sales.

To remove a filter, either click the same item you clicked to invoke the filter (this acts as a toggle) or click any empty area of that visual.

Displaying Quantity by Month in a Line Chart

Lastly, we will create a Line Chart visualization that displays the quantity sold by month.

Pointing to the Line Chart icon in the Visualizations library.
Line Chart in the Visualizations library.

Place a check next to the [Month] field in the Date Hierarchy and the [Quantity] field of the “InvoiceData” table.

Line chart showing Quantity by Month.
Line chart showing Quantity by Month.

Finishing Touches

Adding a Report Title

We can add a header to the report using the Text Box feature located on the Home ribbon.

Text box in the Power BI ribbon (Insert group).
Text box in the Power BI ribbon.
Fragment of a Power BI report titled "Sales Report", with an arrow pointing towards the title on top of the report page.
Title added to the report using a text box.

Adding KPIs (Key Performance Indicators)

A KPI allows us to track a specific single value that is of high importance.

If we want to track the total quantity sold, we can add the Card visual and set its analysis field to [Quantity].

Pointing to the Card icon in the Visualizations library.
Card in the Visualizations library.
Pointing to a card visual showing Total Quantity on a Power BI report page.
Card visual showing Total Quantity.

Rename the Report

Double-click the page tab on the bottom and rename it from “Page 1” to “Report”.

Pointing to a Power BI page tab called Report.
Renaming a Power BI tab.

Save the file with a name of your choosing.

Publishing Your Report

If you need to share your report with others in a way that provides them full interactivity, you’ll need to publish the report.

An advantage to publishing is that you only need to send the viewers a link to the report, not the Power BI file itself.

This link will display the report in the viewer’s web browser.

Another advantage is that when you make changes to the report or update the report data, you don’t need to send out a new file or link to the viewers.  Just update the published report and they will see the latest version.

To publish the report, click Publish in the upper-right of the Home ribbon.

Publish button on the Power BI ribbon in the Share group.
Publish button on the Power BI ribbon.

Select your desired workspace created in your Power BI Service account (with a free account only “My workspace” is available).

Select a destination in the Publish to Power BI dialog box.
Selecting a workspace to publish the report.

When the upload completed, you will see a notification that the report has been successfully published to Power BI Service.

You can click the supplied link to immediately view the published report in a web browser.  This allows you to see what the viewers will see and check for any irregularities.

Message box announcing successful publishing of the report to Power BI. Arrow pointing to the "Open 'Report.pbix' in Power BI" link.
Report successfully published to Power BI Service.
Sales Report viewed in Power BI Service.
Published Sales Report viewed in Power BI Service.

Sharing the Report with Others

To share the report via a web link, click on the Share button at the top of the window.

Pointing to a Share button in Power BI Service.
Share button in Power BI Service.

From the sharing dialog box, either enter the email address of the person you wish to share the report with or copy the link to be used in an email or instant message app.

You can also start a new email message using Outlook or share the report via Microsoft Teams.

Send Link dialog box set to sharing with People in your organization with the link can view, share, and build new content.
Send Link dialog box.

You can even embed an interactive report in a PowerPoint presentation.

Explore more amazing features of Power BI with our tutorials, or check out this FREE Power BI Quick Start Course.

Practice Files

Download the Invoice Data HERE.

And Customer Master Data HERE.

Leila Gharani

I'm a 6x Microsoft MVP with over 15 years of experience implementing and professionals on Management Information Systems of different sizes and nature.

My background is Masters in Economics, Economist, Consultant, Oracle HFM Accounting Systems Expert, SAP BW Project Manager. My passion is teaching, experimenting and sharing. I am also addicted to learning and enjoy taking online courses on a variety of topics.