100% Stacked Column Chart: Visualizing Data Proportions
Sometimes, it can be difficult to express complex data, particularly when demonstrating part-to-whole metrics and comparisons across categories. The 100% stacked column chart offers a clear view of subcategory distribution, simplifying trend analysis. In this blog, we’ll show you what this chart is and how to use it to uncover patterns in your data, as well as show you some examples.
What is a 100% stacked column chart?
A 100% stacked column chart displays columns as wholes with segments indicating the proportions of subcategories within each whole. It visually shows the percentage of the whole that each subcategory constitutes, with the whole always being 100%, for different series.
Benefits of visualizing your data through 100% stacked column charts
Comparative analysis: They enable the comparison of percentages that each component contributes to the total across various series or time periods.
Visibility of trends: Changes in each segment’s relative size over time or across series can highlight emerging trends or shifts in your business makeup.
Clarity in proportions: By setting the wholes to 100%, these charts help understand how significant the parts are in relation to the wholes across different series or time frames, rather than focusing on what the totals for the series are.
Data consolidation: They provide a compact visualization of complex datasets, which can include multiple subcategories in one chart, facilitating easier interpretation.
Facilitates decision-making: Business leaders can swiftly evaluate and respond to their business segments’ varying importance, which is crucial for resource allocation and strategy.
Effective communication: The charts effectively communicate data to stakeholders.
When to use a 100% stacked column chart
- When illustrating components’ contributions to wholes across categories.
- When comparing the distribution of multiple categories within different groups.
When not to use a 100% stacked column chart
- When data points are not parts of a whole.
- When you have large datasets.
- When there are negative values.
- With time series data.
- When comparing the series’ totals instead of their component parts.
Best practices when using a 100% stacked column chart
Choose the right data: Use for parts-to-whole relationships where the cumulative total isn’t important, but the proportions of the contributing categories are.
Limit categories: Keep categories to a minimum to maintain readability; ideally, no more than 4 or 6 stacks.
Consistent order: Maintain the same order of segments across all columns for easy comparison.
Use color wisely: Apply a distinct color palette but ensure it’s colorblind-friendly. Use contrasting colors for different categories.
Label clearly: Directly label the stacks or use a clear legend for quick identification of categories.
Avoid overcomplicating: Refrain from adding too many data points or complex patterns that can confuse the viewer.
Provide context: Add a descriptive title, label axes, and include annotations to clarify the chart’s meaning and highlight key trends.
How to configure a 100% stacked column chart in Bold BI
Following is a GIF which visualizes how you can create 100% stacked column chart.
Steps to configure a 100% stacked column chart in Bold BI
- Connect to your data source in the Bold BI Dashboard Designer.
- Drag the 100% stacked column chart from the visualization panel to the design canvas.
- Drag the relevant fields into the Value and Column boxes.
- Customize the appearance, axis titles, and legend as needed.
- Preview the chart and adjust the settings for clarity and visual appeal.
- Save the widget.
To learn more about the configuration process, check our help documentation.
Use cases of 100% stacked column chart
IT: SLA Status by Project
The SLA Status by Project metric tracks the adherence to service-level agreements for various IT projects, indicating compliance and performance levels. Visualizing this metric in a 100% stacked column chart allows for easy comparison of SLA fulfillment across projects, highlighting areas needing improvement.
Survey analysis: Alternative product choice by age group
Analyzing alternative product choice by age group in a survey analysis helps to identify diverse consumer preferences, which is important for targeted marketing efforts. Visualizing this data through a 100% stacked column chart provides a comprehensive view of relative preferences across different populations, enabling strategic decision-making.
In conclusion, A 100% stacked column chart clearly visualizes the relative distribution of data categories in wholes, enabling end users to quickly compare proportions across different groups. Bold BI enables easy creation of this chart, aiding in identifying trends and making data-driven decisions efficiently. Leverage the power of Bold BI’s 100% stacked column chart in your application to improve your data visualization precision.
Originally published at https://www.boldbi.com on March 8, 2024.
