Dashboarding and Reporting with Power Pivot and Excel by de Jonge Kasper

Author:de Jonge, Kasper
Language: eng
Format: epub
Publisher: Holy Macro! Books
Published: 2014-04-08T16:00:00+00:00

Figure 4.36: Adding a PivotChart.

To optimize his space, Jim decides to remove the field buttons. He removes the field buttons by selecting Analyze, Field Button, Hide All. Jim also decided to change the chart style to a line chart.

Dashboard Tip: Choosing the Right Chart

Choosing the right chart to show data might be the hardest problem in data visualization. Unfortunately, there is no surefire way to choose the right chart. It depends on many, many factors—and on the data you want to show. You have to think about what information you want to show and what information is important. The core idea behind graphs and charts is that they help people understand data quickly and allow you to tell the story behind the data. Therefore, an important factor in choosing and designing the right chart is having a good understanding of the data and the types of charts.

There are four core types of chart visualizations: those that show the distribution of data points, those that make a comparison between data points, those that show the relationships between data points, and those that show how data points are put together (composition). These visualizations help your audience see what you are talking about.

Let’s look at an example for each type:

• Comparison: You use this type when you want to compare two or more data points, such as the revenue for each month between years, revenue by region, or revenue for each month for the current year. This is the most common type and is usually a line, bar, or column chart.

• Distribution: This is the second most common chart category. As the name suggests, a distribution chart is used to display how data is distributed and to understand outliers and categories that are outside the norm (for example, distribution of voters per region, types of returned products over the past month).

• Relationship: This type shows interesting relationships that can lead to new understanding about correlations and causality between a wide ranges of variables. For example, you might use this type of chart if you want to prove whether hours of study make for better results or to show the relationship between in-store sales and holidays. The most common relationship charts are scatter plots and bubble charts.

• Composition: This type of chart allows you to display how specific data compares to broader data (for example, what browser types are visiting a website, product sales as a percentage of total revenue). Commonly used composition charts are column charts, bar charts, and pie charts.

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