Power BI has a large array of visualizations that can be used to help businesses find more effective ways of delivering the data they collect. From standard bar charts to AI powered visuals like decomposition trees, there is so much that Power BI can do. As we go through the visuals, there are 10 main sections that we will cover in the following order: Bar and Column; Line and Area; Waterfall, Funnel, and Scatter; Pie, Donut, and Treemap; Maps; Gauge, Card, and KPI; Slicer; Table and Matrix; and AI Visuals. In order to keep this from being 7000+ words I am going to break the sections into 3 blogs.

Getting right into it, we have the Bar and Column charts. In this group of visuals, you have the stacked bar/column charts (highlighted in red in Figure 1), clustered bar/column charts (highlighted in blue in Figure 1), and the 100% stacked bar/column charts (highlighted in green in Figure 1).

Stacked Bar charts and Stacked Column charts are highly effective when you need to examine both the total value of a category, such as sales (x-axis for bar charts and y-axis for column charts), and the specific subcategories within it, such as the products being sold (displayed in the legend). The y-axis reflects the variable you are analyzing, which corresponds to the x-axis and legend, as exemplified in both the “Sum of Sales by Country and Product” bar chart and “Sum of Sales by Month and Product” column chart that I have created (highlighted in red in Figure 1 above).

Next, we have Clustered Bar charts and Clustered Column charts (highlighted in blue in Figure 1 above), which offer a straightforward comparison of multiple categories or series within a specific category. For example, I have the “Sum of Sales by Product” chart which shows how different products sold compared to each other. Additionally, I have the “Sum of Sales by Month” chart which allows you to see which month of the year has better sales than the others.

Concluding the Bar and Column charts section, we now turn our attention to the 100% stacked bar/column charts. These charts are used in a similar manner to the standard stacked bar/column charts, with the key distinction being that they display various data points as a percentage of the whole, totaling 100%. In Figure 1 above, as highlighted in green, I created “Sum of Sales by Year and Country” and “Sum of Units Sold by Month and Product” as 100% stacked bar/column charts. In the former charts, which are Stacked Bar and Stacked Column charts (highlighted in red in Figure 1 above), I use the “Country” as the legend data point so I can see which country had the highest percentage of sales per year. Conversely, in the latter charts, I use the “Product” as the legend data point, facilitating a visual analysis of how different products performed on a month-to-month basis.

Moving on from the Bar and Column charts, we have the Line and Area charts. In this group we have Line charts (highlighted in red in Figure 2), Area charts (highlighted in blue in Figure 2), Stacked Area charts (highlighted in green in Figure 2), Ribbon charts (highlighted in purple in Figure 2), Line and Stacked Column charts (highlighted in orange in Figure 2), and last but not least, the Line and Clustered Column charts (highlighted in black in Figure 2).

To start we are going to look at the Line chart (highlighted in red in Figure 2 above). Line charts are great for looking at how specific metrics change based on time. In this case I used the line chart “Sum of Sales by Year and Month” to show the change in the “Sum of Sales” from month to month across both of the years in the data set.

Moving on, we will look at the Area chart (highlighted in blue in Figure 2 above). Area charts share a close resemblance to Line charts, as both are intended to illustrate how particular metrics evolve over time. However, Area charts are tailored to provide additional emphasis on the data being presented in the visualization by filling in the “Area” beneath the line’s path.

Next, let’s examine the Stacked Area chart (highlighted in green in Figure 2 above). For this example, I created a chart titled “Sum of Units Sold by Year, Quarter and Discount Band”, which reveals that the chart incorporates four distinct metrics. The purpose of this chart is to see how many units sold in each discount band and how they sold from quarter to quarter over the years. The Stacked Area chart is basically the area chart and the stacked column chart fused together. We can use this visualization to observe how all the various subcategories within the legend, labeled “Discount Band, add up to the whole of the y-axis, represented as “Sum of Units Sold, across the x-axis, which, in this instance, includes “Years” and “Quarters.

Now we will discuss the Ribbon chart (highlighted in purple in Figure 2 above). The Ribbon chart is used to show the rankings of different categories over time. In the case of this blog, I used the Ribbon chart to show the rankings of the different products being sold from year to year, presented as “Sum of Sales by Year and Product.

In the final category within the Line and Area charts section, we have the Line and Stacked Column chart, as well as the Line and Clustered Column chart (highlighted in black in Figure 2 above). In these two visuals, we observe the Line chart illustrating changes over time or across different products. In addition, we have the columns, which represent various metrics such as “Sum of Gross Sales” in the Line and Stacked Column chart, where it’s titled “Sum of Sale Price and Sum of Profit by Product, and “Sum of Sales” and “Sum of COGS” in the Line and Clustered Column chart titled “Sum of Sales, Sum of COGS, and Sum of Gross Sales by Product. These combo charts are used to show how multiple different metrics can affect each other while still delivering the necessary results. Combining both charts into a single view allows for a more efficient and immediate comparison of the data.  In the Line and Stacked Column chart, we can easily discern that for Paseo, both Profit and Sale Prices were at their highest, while in contrast, for Carretera, Profit was notably lower than the “Sum of Sale Prices.”

Finally, concluding this blog, we will explore the Waterfall, Funnel, and Scatter charts. In this section, we have the Waterfall chart (highlighted in red in Figure 3), the Funnel chart (highlighted in blue in Figure 3), and the Scatter chart (highlighted in green in Figure 3).

To open up this section, we will hop right into the Waterfall chart (highlighted in red in Figure 3 above). In the chart titled “Sum of Profit by Segment, you’ll notice four green bars, one red bar, and a blue bar. The green bar indicates the amount by which profit is increasing within its respective category, while the red bar represents the profit loss within its category. The blue bar illustrates the overall net increase or decrease across all categories. The Waterfall chart is great for visuals like this because they show the running totals while accounting for both additions and subtractions from the overall amount. These charts are also sometimes referred to as Bridge charts because of their characteristic shape.

Next, we will cover the Funnel chart (highlighted in blue in Figure 3 above). My Funnel chart, titled “Sum of Sales by Product,” is being used to show comparative values between the total sales across the different products. The largest selling product gets counted as 100% and all of the other products are labeled by what their percentage value is compared to the largest selling product. In this scenario, when analyzing the chart, we see that Carretera is 41.9% of Paseo. Funnel charts are usually used for processes that have different stages. Another way it could be useful is in a pear shape which enables the user to see where a problem could be along the process flow.

For the final section to be discussed, I will now go over the Scatter chart (highlighted in green in Figure 3 above). In my Scatter chart, titled “Sum of COGS and Sum of Profit by Month and Product, we see a bunch of different dots. Scatter charts are used to display a large quantity of data points in order to find trends in data over time or other metrics. Scatter charts can be useful because this is probably one of Power BI’s best charts to show relationships between two metrics and are great for evaluating a large number of data points by looking at clusters.

For all who read through all of this, I would like to thank you for your time and I hope that you can walk away saying: “You learn something new every day.” If you would like, I have a blog about how you can add these different visualizations into Power BI which you can read by clicking here. If you would like to keep reading on the different visualizations, next up I will cover the Pie, Donut, and Treemap section, following that I will talk about the different kind of Maps that Power BI has to offer and to finish that blog I will go over the Gauge, Card, and KPI section. To read that you can click here. Thanks again everyone and I hope you all have a great day.

Bailey McDonald
Data Engineer, Patriot Consulting
Email: bkmcdonald@patriotconsultingcorp.com | Blogs: Patriot Consulting Blogs
LinkedIn: Personal: BaileyMcDonald | Company: Patriot Consulting

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