Data Storytelling — How to Choose the Right Chart or Graph for Your Data

If you have data you want to visualize, make sure you use the right charts. While your data might work with multiple chart types, it’s up to you to select the one that ensures your message is clear and accurate. Remember, data is only valuable if you know how to visualize it and give context.

What story does your data tell?

Before making a chart it’s important to understand why you need one. Charts, maps, and infographics help people understand complicated data, find patterns, identify trends, and tell a story. Think about the message you want to share with your audience.

Follow best charting practices. Your numbers need to add up and charts need to be scaled accordingly. What would you like to show? There are five main types of charts:

Comparison

Comparison charts are used to compare one or more datasets. They can compare items or show differences over time.

Charts are perfect for comparing one or many value sets, and they can easily show the low and high values in the data sets. To create a comparison chart, use these types of graphs:

• Column
• Mekko
• Bar
• Pie
• Line
• Scatter Plot
• Bullet

Relationship

Relationship charts are used to show a connection or correlation between two or more variables.

Relationship charts are suited to showing how one variable relates to one or numerous different variables. You could use this to show how something positively effects, has no effect, or negatively effects another variable.

When trying to establish the relationship between things, use these charts:

• Scatter Plot
• Bubble
• Line

Composition

Composition charts are used to display parts of a whole and change over time.

Use this type of chart to show how individual parts make up the whole of something, such as the device type used for mobile visitors to your website or total sales broken down by sales rep.

To show composition, use these charts:

• Pie
• Stacked Bar
• Mekko
• Stacked Column
• Area
• Waterfall

Distribution

Distribution charts are used to show how variables are distributed over time, helping identify outliers and trends.

Distribution charts help you to understand outliers, the normal tendency, and the range of information in your values.

Use these charts to show distribution:

• Scatter Plot
• Mekko
• Line
• Column
• Bar

Analyzing Trends

If you want to know more information about how a data set performed during a specific time period, there are specific chart types that do extremely well.

You should choose a:

• Line
• Dual-Axis Line
• Column

Different Types of Graphs and Charts for Presenting Data

1. Column Chart

A column chart is used to show a comparison among different items, or it can show a comparison of items over time. You could use this format to see the revenue per landing page or customers by close date.

2. Line Graphs

The most common, simplest, and classic type of chart graph is the line graph. This is the perfect solution for showing multiple series of closely related series of data. Since line graphs are very light-weight (they only consist of lines, as opposed to more complex chart types, as shown further below), they are great for a minimalistic look.

3. Bar Graphs

Bars (or columns) are the best types of graphs for presenting a single data series. Bar charts have a much heavier weight to them than line graphs do, so they really emphasize a point and stand out on the page.

4. Combo Chart

The above two types of graphs can be combined to create a combo chart with bars and lines. This is very useful when presenting two data series that have a very different scale and might be expressed in different units. The most common example is dollars on one axis and percentage on the other axis.

5. Scatterplot

The scatterplot is excellent for showing the relationship between two data series and determining their correlation. The scatterplot is great for showing what a distribution of data points looks like and for drawing a line of best fit for regression analysis.

6. Waterfall Chart

In Excel 2016, Microsoft finally introduced a waterfall chart feature. In all older versions of Excel, analysts had to create a custom workaround using stacked column charts. If you are in a version of Excel prior to 2016, then please see our free guide and waterfall chart template. The waterfall chart is excellent for variance analysis and explaining how an “actual” result was different than a “budget” or how something has changed relative to an original data point.

7. Pie Graph

Pie charts have a bad reputation and are known for being messy and hard to read. However, if you’re trying to illustrate the percentage breakdown of a small number of data points, then they can be very effective. For example, the percentage of people who prefer bananas, pineapples, and grapes.

8. Histogram

Histograms are a type of graph that shows the distribution of a dataset. They graph the percentage or the number of instances of different categories. For example, to show the distribution of age categories (0–10, 11–20, 21–30, etc.), we can clearly see which categories are the biggest and how many people fall into each.

9. Gauge Chart

The gauge chart is perfect for graphing a single data point and showing where that result fits on a scale from “bad” to “good”. Gauges are an advanced type of graph, as Excel doesn’t have a standard template for making them. To build one you have to combine a pie and a doughnut. Learn how in our data visualization course.

10. Area Graph

An area chart is a solid area and can be effective when showing stacked, cumulative data series — for example, showing the cumulative sales revenue from different products. This allows the reader to easily visualize the “area” (or weight) of each series relative to each other.

11. Spider Chart / Radar Graph

A spider or radar graph is a very useful type of graph for showing qualitative data or the overall “score” or comparison of multiple series. For example, a spider/radar can be easily used to compare three different types of phones based on five criteria (speed, screen size, camera quality, memory, apps).

Conclusion

There are hundreds of modern-age data visualizations. Developers and information designers can go beyond just bar graphs and line charts to display insights as data stories.