Power BI Basics Guide Ask Us

General Overview

Welcome to this Power BI Basics help guide*. This guide will provide information on:

• how to ingest data in Power BI;
• how to link your data tables;
• how to create visual elements;
• how to create slicers;
• how to share your report to your audience.

For information on how to create data visualizations, click here to access our Microsoft Excel Basic Visualizations Guide. This is in Excel but the guidelines are also applicable for Power BI. If you would prefer to consume much of the material offered in this guide via video, you can click here to view my Introduction to Power BI video series.

Basic components of a Power BI report

Power BI is best known for creating interactive reports and dashboards. It is freely available to all UNB students, faculty and staff by downloading the MS Office suite at unbcloud.sharepoint.com/sites/ITSSelfServe. In this research guide, we will scratch the surface of this powerful software program to get you going and hopefully set you up for future exploration of Power BI on your own.

Let’s take a look at the Power BI environment so that we all have a common language and understanding of what is what. At the top of the worksheet, you will see a series of tabs (File, Home, Insert ...). Yours may or may not have all the same components as the image but the concept here is the same across all Power BI packages. 

Clicking on a tab will reveal its associated ribbon which includes the relevant command button grouped according to related function. Each tab has its own specific set of buttons in its ribbon.

The main white space on the screen (highlighted in yellow in the image below) is called the Report Canvas. This is where the different elements of your report will ultimately appear. The default view is the Report view but you can also see the Table view (which will show your data in a table format), the Model view (which will show relationships between your data tables if you have more than one data table and you want to relate one to the other) and the DAX view (DAX is the programming language that undergirds Power BI). You can switch views by clicking on the relevant button on the left side of the screen.

At the bottom of the report canvas, you will see a tab with the name Page 1. You can add a page by pressing the small "+" icon next to the Page 1 tab. You can delete a page by clicking on the X when you hover over the tab. You can rename a page either by by right clicking on it and choosing rename on the menu or simply by double clicking on it and typing in a new name. 

On the right hand side, there are three panes (sometimes, you only see one when you first open the software). The Data pane is where the data you ingest in Power BI will eventually be visible. The Build pane gives you access to a variety of visualizations (some of which are not available in Excel) as well as integration with the R and Python programming languages. The Format pane allows you to format your report canvas as well as your individual visualizations. On the right hand side, you have the ability to expand and collapse these panes to best suit your needs at any given time.

At the top of the screen, you will see the file name of your Power BI file (also known as your Power BI report).

*Compatibility

It is important to know that Power BI is not a static program and does come in many different flavours: Power BI for PCs has various versions and while most of the functionality is retained from one version to the next, each iteration creates some changes to the platform. Other visualization programs (such as Tableau) do have many of the same features as the ones we will be discussing in this help guide but the mechanism to activate them may be very different. Please be aware of the program and version of Power BI you are using. For your reference, we will be using Power BI for Microsoft 365 (PC version) with the on-object interactions activated (File/Options and Settings/Options/Preview Features) as our help guide version. 

Ingesting Your Data

In this section, you will learn how to ingest your data in Power BI. So how do we do it exactly?  

Using Power Query

Power BI uses Power Query to get data. If you’ve already used Power Query in Excel, you’re ahead of the game. If not, Power Query is simply a package of tools that is used in both Power BI and Excel to get data and transform it in a systematic way. We’ll take a very quick look at how to use Power Query here but it is worth looking at its many features as it can be a huge time saver, especially with larger datasets. 

To access the Power Query options, simply go to this section in the Home tab ribbon of Power BI (highlighted in yellow below).

From there, you can click on the “Get Data” button (first one on the left). When you do, you will see that you can select from a wide range of data sources.

In this example, our data is in an Excel workbook so we will click on that option at the top of the list and then press the “Connect” button at the bottom right corner. We will then need to select our workbook using the File Explorer on our computer and once we have located it, we can click on the “Open” button.

The first screen that pops up after we do this is the Power Query preview screen where your data will be displayed. Power Query recognises the first three elements as table because I made them into official Excel tables when I created the Excel workbook. It also recognises all the sheets within the workbook. 

When we click on a table name (or a sheet name), we can see all its elements previewed on the right pane. 

We are really only interested in the tables for this example so we will select all three of them and leave the sheets unselected. 

If I wanted to, we could clean or transform the data in these tables by pressing on the “Transform Data” button at the bottom right section of the page. It’s usually good practice to do this but since this is not a Power Query research guide, and that I am satisfied with the data structure for now, I will click on “Load” to load these tables into Power BI. 

Once this is done and the data is loaded, we can see each table in the Data pane on the right side of the Power BI screen (highlighted in yellow below). We can expand the tables to reveal the columns or fields within each table.   

Now that the data has been ingested in Power BI, we are ready to play around with it in this software. 

Refining our Data

Now that we have our data ingested, and before we start linking data tables, let's refine our data a little. To do this, let's shift to Table view of Power BI by pressing on the table view button on the left-hand side of the screen. Here we can see that we are looking at the StudentProfile table and we have all the associated fields.

If we click on a field heading, the associated ribbon changes. We have the field name, the data type (in this case text but we can click on the dropdown menu if we wanted to change it), and a whole host of other options. Make sure that you are happy with your data elements but your data type especially. If you cleaned your data in Power Query before ingesting it here, this step will be much shorter and it's just a matter of double-checking that everything transferred over correctly. 

The data category section is often denoted as “uncategorized”. You can change that by clicking on the dropdown menu. Sometimes it makes sense to change it (as above with City) and sometimes no good option is available so leaving it as uncategorized is fine. If you put a geographic category on a field, a little globe will appear in front of the field in the Data pane. Just an FYI that changing the data category does not impact the data type. The data type remains the same, the data category is just an enhancement, an additional piece of information on top of the data type for that field.

When dealing with quantitative data, make sure you take a look at the summarization option (on top of data category). Use the dropdown menu to dictate if the numbers should be summed, averaged, counted, minimum, maximum or not summarized. If it is summarized, a little sigma (Σ) will appear in front of the field in the Data pane. If it is not summarized, the sigma will disappear.

Linking Data Tables

Now that we have our data refined, let's link our data tables. To do this, let's shift to Model view of Power BI by pressing on the model view button on the left-hand side of the screen. This allows us to view relationships between our data tables.

Automatic relationships

We can see our three tables here and can spot that two of them (the TestScore table and the StudentProfile table) are connected. This is a relationship and if we click on it, it highlights the fields that form that connection.

In this case, Power BI was able to figure out automatically that the StudentID field in the StudentProfile table is linked with the StudentID field of the TestScores table. The 1 at the base of the relationship next to the StudentProfile table means that there is only 1 instance of StudentID in that table so every StudentID in that table is unique. The asterisk at the end of the relationship next to the TestScores table means that there are many instances of the same StudentIDs in that table. This is known as a one-to-many relationship. 

Manual relationships

The SubjectCategory table is just floating there even though there is a common element between it and the TestScores table. The Courses field in the TestScores table and the Subject field in the SubjectCategory table are the same so we can link these two. Power BI has not done it automatically because the fields are not called the same thing but we can do this manually by simply clicking and dragging one field over the other (it does not matter which one you choose). When we do, a new relationship pane appears and when we press the "Save" button at the bottom of it, we can see the new relationship which is also a one-to-many relationship.

Types of data tables

A quick aside: when we talk about tables, we sometimes refer to fact tables and dimension tables. Fact tables are tables that have numeric data, such as the TestScores table.  Dimension tables add additional descriptive information in the way of qualitative data. So the StudentProfile and SubjectCategory tables are dimension tables. 

Ok, this is great. Now our fact table (TestScores) is linked to both dimension tables (StudentProfile and SubjectCategory). We are now ready to use our linked data tables to create visual elements for our Power BI report.

Creating Visuals for Your Report

Report View

As a refresher, the main white space in the Report View on the screen is called the Report Canvas. This is where the different elements of your report will ultimately appear. On the right, we have our Data pane which shows us what data (in this case our data tables) we have ingested in the current session of Power BI. These are expandable so we can see what fields are in each of the associated tables as you can see with the StudentProfile table.

Next to that, we have the Build pane which house the visualizations we can use in our Report Canvas to represent our data. To the left of that, we have the Format pane which includes formatting tools for individual visualizations as well as the Canvas Report itself. Finally there is a Filters pane (collapsed but visible next to the Format pane in this picture above) which we will discuss a little later in this guide. You can expand and collapse these panes by pressing on the icons on the right side of the Data pane or by pressing on the arrows symbol next to the pane title name.

Creating visualizations

There are two main ways to create a visualization in Power BI.

• You can click on one of the visualization buttons in the Build Pane and then either click and drag your data field(s) onto the visualization itself or drag and click them in the data field section(s) within the Build pane. In the former case, Power BI will guess which field the data will appear. If it guesses incorrectly, you can always change it manually (see moving data fields section below).
• You can also click on a data field from your data in the Data pane and Power BI will guess as to what is the most appropriate visualization for that type of data. Again, if you are not satisfied with the visualization it has chosen, you can always highlight your visualization by clicking on it, and select another visualization button in the Build pane to change it to your desired visual.

Editing the data in your visualization

Once you created the shell of your visualization, you may want to edit the data within it. Below, we have an example of a card visualization which is the representation of a single data point expressed on a card-like format. You can see, in the picture below, that the Score data point from the TestScores table is being selected because it is highlighted with a green checkmark in the Data pane.

Power BI assumed we wanted to see the sum of all the scores. If we wanted to change that to another summarization, we could click on either the "Sum of Score" data field in the Suggest a visual helper pane next to the visual or the "Sum of Score" data field in the the Build pane. From there, we have several options to choose from. Each visual and data field will have a different set of options to choose from so it is important to verify what options are available to you at any given instance of visualization creation. 

Adding or moving data fields in your visualization

If we wanted to add another data series to a visualization element, we could:

• click on the visualization and then click on a data field in the Data pane. Power BI would guess as to what the most appropriate field in the Build pane it belongs to. Power BI usually does a good job of guessing the appropriate Build pane field but if it gets it wrong, you can always click and drag it to the appropriate area of the Build pane;
• drag a data field directly on the visualization. Again, Power BI would guess as to what the most appropriate field in the Build pane it belongs to;
• drag a data field directly in a field in the Build pane. This is the option I recommend since you get exactly what you are looking for. In the picture below, I am dragging the Subject data field from the SubjectCategory table in the Y-Axis field of the Build pane.

Formatting visualizations

Each visual element will have some formatting features – some have a few and some have quite a bit. They appear in the Format pane on the right of the canvas. We will go over a few examples here but but I would encourage you to explore the formatting options of the visuals you are working with and play around to see how you can make them as powerful as possible. Here are a few categories that appear with many (but certainly not all) visualizations. 

Size and positioning

This allows you to change the size of the visual by adjusting the pixel width and height as well as the position of the visual on the canvas. Both of these can also be done manually by clicking and expanding/collapsing or clicking and dragging the visual respectively. Additionally, this section allows you to modify the visual's padding, shadow, border and background colour elements.

Title

This section allows your to include or exclude a title by turning the toggle either on or off. If you include one, you can modify the title text, the font size, font colour, font type and the title alignment and spacing. You can also include or exclude a subtitle and have access to the same modifications as you do with the title. 

X and Y axis

This allows you to modify elements for the respective axis including turning the axis values on or off by using the on/off toggle. If they are turned on, you can adjust the axis range, the font format, the axis title and the layout. 

As mentioned earlier, there are sometimes many different options for a visual (as you can see from the picture above) and it is worth spending some time to explore each of them in order to have the knowledge on how each visualization can be optimised for your particular target user(s). 

Interactivity

Notice that all the visuals in the report are interactive. If I were to click on Fredericton in the map visual for example as I do in the picture below, the Fredericton data is put forward. The median score card, the number of students, the median score by subject and the Median score by exam month all pertain to Fredericton. We could do this for another city on that visual or click on an element of another visual (like the bar chart or the line chart) and the mechanism would be the same.

We could also use the Control button on our keyboard to select multiple elements. For example, if I wanted to get all the language elements, I could click on the English bar of the bar chart visual and while pressing the Ctrl button on my keyboard also click on the French bar of the bar chart and the data relevant to those two elements would be put forward across the report.

Time savers

If you have already built a visual that you want to retain the same structure (create a second bar chart but with different data for example), you can simply click on the original visual to select it, copy it (Ctrl + C) and paste it (Ctrl + V) and you have another visual with the exact same format as its predecessor. All you have to do now it reorganise its underlying data by removing the original data field and placing the new data series in the relevant data field.

Similarly, when creating a visual, you can use the format painter button in the Home ribbon to transfer the formatting from a pre-existing visual onto the new visual. Simply click on the source visual (the one whose format you wish to copy), then click on the Format Painter button on the left-hand side of the Home ribbon. Finally, click your format painter mouse on the target visual (the one you want to adopt the existing formatting). Now the second visual has the same format as the original one without having to do all the formatting modifications from scratch.

Adding Slicers to Get More Insights

In this section, we will discuss how to filter our data in order to get more granular insights. There are several ways to do this in Power BI. One of them is to use filters. This can be done by using the Filters pane (highlighted in the picture below) which can filter data at the report level (all pages in the example below), the page level (this page in the example below) or it can be done at the individual visualization level (first highlighted field in the example below) by using the Filters pane when the visualization is highlighted. Pressing on the filter icon of the visualization itself (see image below) will also reveal if and what data is currently being filtered.

Slicers are another way to filter your data. I prefer slicers because it makes the filtering explicit for the user and is more intuitively interactive than the classic filtering method. 

How to create slicers

To create a slicer, you simply need to have some space in your report (make sure that another visualization is not highlighted) and click on the slicer icon in the Build pane (see highlight on the image below). This creates the shell of the slicer. You can now add the data series you wish to use as your filter in the field section of the Build pane (see highlight on the image below).

Slicer interactivity

In this case, I'll pick the “City” data series as my filtering data (see highlight below). Now if we click on “Saint John”, for example, every card and visual adapts to the slicer (see image below). We see that there are 272 students that have a median score of 75 and below that are the map visualization, the median score by subject and exam month specifically for Saint John. You can press the Control button on your keyboard to do multiple selections.  When you are done filtering, you can click on your original selection to return to a non-sliced view of the data. 

Formatting slicers

Just like our visualisations format, we can format slicers using the Format pane. Below we can see a series of formatted slicers that all interact together. In this case we can use the slicers to take a look at the Fredericton student population that is male and in junior levels of high school (grades 9 and 10) fared in the Humanities subjects.

So overall this dataset shows that they performed at a median score of 73, which is 2 points lower than their counterparts in the province and also 2 points lower than the overall provincial average. We can also quickly see that French is a laggard for this population and that median scores varied significantly depending on the exam month. All interesting insights that should lead to more questions being posed. That is the main advantage of using slicers: it allows you to drill down in the data with a few clicks and represent this data visually in order to gain insights.

I am currently working on the Power BI basics guide. Thank you for your patience.

Look for the next video in the Power BI introductory series: "How to share a Power BI report to your audience" premiering Thursday, November 20th, 2025 at 10am AT.

The associated tab of this guide will also be released on the same day.