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Visualizations

Contents

keyboard_tab
Course Introduction
Primary Power BI Components
2
Visualizations
PREVIEW5m 33s
3
Filters
PREVIEW1m 22s
Visualizations
Overview
Difficulty
Beginner
Duration
35m
Students
113
Ratings
5/5
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Description

Power BI has changed the BI landscape forever, enabling BI professionals and regular Excel users alike to work with big data and build insightful dashboards. 

Learn to use this powerful business intelligence solution from the ground up. Navigate the intuitive user interface and explore the ecosystem of data modeling tools. Discover outside-the-box visualizations and broadcast your insights to colleagues in the Power BI Service. This course gives you a solid foundation to begin your Power BI journey. 

Learning Objectives

On completing this course, learners will be able to:

  • Identify the primary components of the Power BI interface: reports, data, and model views
  • Import Excel data and build basic visuals
  • Publish a desktop report to the Power BI Service
  • Identify common challenges in Power BI data models, implement smart solutions, and avoid common mistakes

Intended Audience

  • Business professionals whose job requires them to design, build, or deliver business intelligence metrics
  • Anyone preparing to take the Microsoft PL-900 exam

Prerequisites

A desire to learn to use Power BI

Transcript

To grasp Power BI, and grasp it quickly, it's helpful to have an awareness of the primary components that make up Power BI. The first we will be discussing, and arguably the most identifiable, is the Power BI Visual, or Visualization. These are the charts and graphs, and filters and slicers, and all other visual elements that are used to display business measures, and make that data easily consumable. Easily is the key word here. We want our data to be as easy as possible to understand from a first glance. That's a very high bar and we will not always meet it, but Power BI sure makes it easier than Excel and gives Tableau a run for its money. 

To create a visual, simply select the visual type from the visualizations pane. After that, select the data you want to be displayed on the visual from the fields pane. If you simply check the box, Power BI will make a guess where this field should go, but you can also drag and drop to position fields exactly where you want them. The following is an oversimplification, but in general, the Legend area and Axis areas are where you drop the categories that you want to segment your data by.

So if I wanted to see the gross sales separated by channel, I might drag channel into the Legend area or the Axis area. The Legend area usually segments your data by color, and then the Axis area segments your data by shape. And if you're fancy and want both bars and different colors, you can actually drag the channel field into as many different areas as you want. So even though my channel field is already in the Axis, I can add it again into the Legend. And now each segment has its own bar and its own color.

Most visuals will also have a Values area. If you're familiar with Excel PivotTables, this operates like the Values field area in PivotTables, but if you're not familiar, then essentially, the Values area is where you should place the calculable data. Usually this is your numerical data. In this case, I am adding up the gross sales. Gross sales is a calculable, or numerical field, so it belongs in the Values area. After that, just try different things out and you'll get the hang of it. There are many different types of visualizations within Power BI.

Let's start off with our old friend, the Pie Chart. The pie chart shows the relationship of parts to a whole. Here we are analyzing the number of employees per Region. But the pie chart is slowly declining in popularity, especially for scenarios in which we want to compare each Series, or each Region in this case, to each other. For that we will need the column chart. To switch your visual to different chart type, just select the chart and then simply choose another chart type in the visualizations pane.

Column charts are the standard for comparing series to each other. Here we are counting the number of employees in each region. We can count the number of employees just by counting the number of times a first name is listed. And we're confident about this because we're familiar enough with our data to know that in this table we're analyzing, each person only shows up once. But if we were analyzing a table where employee names might show up lots of times, like a sales transactions table, then we would probably want to use the Distinct Count. The Distinct Count would only count the first time an employee's name showed up. To change the way a field is being calculated, click on that field's dropdown menu and chose the calculation type. For our scenario, Count will do just fine. So we'll leave it.

Now why do we need a Donut Chart if we already have the Pie Chart? The Donut Chart is just a bit easier on the brain. While Pie Charts draw the eye to the center, and your brain will work to calculate the area of each slice, the Donut Chart allows your eye to be drawn to the circumference, and no area calculations are needed by your brain. It's subtle, but some people really feel the difference. Which do you prefer? Another big hitter is our Scatter Plot. This is a type of data visualization that shows the relationship or correlation between variables.

This scatter plot revealed to me that there is a linear correlation between the Manufacturers Suggested Retail Price and the Total Sales that were earned for that product. If you drew a straight line from the bottom left of the chart to the top right, you can use it to tell you about how shelf price predicts Total Sales. We can see here that generally, MSRP does determines Total Sales, because we have a lot of dots falling on this line. Dots that are above this line are products that actually bring in more than normal gross sales per dollar MSRP. So these are our better performers, and these other products are our lower performers.

One of the scatter plot's biggest advantages is that it lets you identify outliers. We can easily see which products are our best performers and which ones are our poorer performers, which allows us to make strategic decisions about those products. And the last one we need to become familiar with is the KPI, or key Performance Indicator. You'll notice there's actually a visual called KPI and that's the visual that you see here. It tracks a trend over time. But there are also other types of KPI, including this popular one, the gauge chart.

KPIs generally show progress towards a measurable goal, and on this gauge KPI, we can see we are about 80% towards our goal this year. And you have tones of customization options with visuals in Power BI. Here we can set the target, we can set custom tooltips, min and max of the gauge, the color, and tons of other parameters, which allow you to tell the exact stories your consumers need.

About the Author
Avatar
Chelsea Dohemann
Senior Technical Trainer and Microsoft Certified Master
Students
580
Courses
2

Chelsea Dohemann is a Senior Technical Trainer and Microsoft Certified Master with almost a decade of experience in technology training. She has taught an array of applications from Microsoft products including Office 365 web apps, Microsoft Office Suite, Power BI, VBA for Excel, and SharePoint to Adobe Acrobat Pro and Creative Cloud. Being a persistent learner herself, Chelsea is acutely in-tune with the challenges of learning. She presents her topics in plain language, with real-world examples, reducing complex concepts down to their simple parts.