12 Best Chart and Graph Types for Actionable Data Visualization (2024)

12 Best Chart and Graph Types for Actionable Data Visualization

You don't have to be a data visualization pro to always pick the best chart and graph types, every single time. You might be surprised to know that you can just follow a simple set of steps to always ensure you are making the right chart and graph choices for your target audience.

Picking the right charts and visualizations in the enterprise can be the difference between delivering business value and landing another failed attempt of insight that adds to the existing scrapheap of data visualization chart junk.

To avoid that, data visualization must be given careful consideration because the benefits of getting it right can result in:

• Attaining business goals aligned to organizational strategy
• Support of value-based action and behavioral change
• Achieving return on data investments

When done right, data visualization can help transform the organization by delivering key information to decision-makers at the right time in an easily consumable format. So, how can that be achieved?

HOW TO CHOOSE THE BEST CHARTORGRAPH TYPES to Use for Your Data

1. Set the strategy and measurable goals for the visualization
2. Understand the audience that will consume the charts and graphs
3. Identify the business questions that they need answered with data
4. Assess and gather the data for what questions can be answered
5. Select the best type of chart or graph to answer the questions
6. Get feedback before starting development

A major reason why picking the right data visualization is hard is due to lack of process. Specifically, not following a methodical approach to requirements gathering. Why? Because the audience needs to be understood, the people consuming the charts and graphs. Indeed, it is challenging to guess what is the best visualization without this approach.

1: Set the strategy and measurable goals for the visualization

Data is used to inform and influence decision-making. There must be a vision behind what data is needed in order to support that decision. This is where, from the outset, a strategic vision is needed.

What is being measured and why? What is the outcome being sought after? Reduction in operating costs? Increased customer satisfaction?

Whatever it is, there must be alignment and consensus on it before it is possible to select the right chart or graph to represent the necessary data.

2: Understand the audience that will consume the chart and graph tYPES

Without understanding who the audience is, their motivations and what challenges they have, greatly increases the chances of not picking the right chart of graph.

Perhaps the audience is a group of financial auditors. If you know auditors, they absolutely have a preference for certain chart types such as tables and waterfall charts. Without this knowledge, picking the right chart for this audience is doomed to failure.

The field of user experience has much to say on this area. A persona design approach should be followed at this stage in order to narrow down the focus of who is being designed for to understand preferences and design criteria.

3: Identify the business questions that they need answered with data

With the previous steps taken, picking the right charts and graphs has become considerably easier. But it is not enough.

Aligned to both the goals from step one, the personas in step two, the set of questions that the data needs to answer must be identified. The questions must be of sufficient detail to assist both in the selection of the data but also in helping to pick the right chart.

For example "How many widgets have we sold so far this year in millions of units?", is a well-formed question that would lead to picking a certain chart type specific to that question.

4: Assess and gather the data for what questions can be answered

The previous steps assumed there were no constraints on what data was available. But now, the data reality must be addressed. Based on a data assessment, the set of questions to be answered are narrowed down to what is feasible in the short term. That is, what can be visualized now versus in later iterations.

Naturally, this must be communicated back to the audience to ensure awareness and alignment with the approach. This is covered extensively in the book Delivering Data Analytics.

5: Select the RIGHT chart or graph TYPE to answer the questions

By following the previous steps, the risk of incorrect chart selection has been greatly reduced, but it is not zero. There are two approaches to selecting the right chart from here.

Firstly,leverage the list of primary and secondary chart types for a simplified set of12 visualizations to use for 90% of situations.

Alternatively, a chart selector could be used to break up the chart types by:

• Composition
• Comparison
• Distribution
• Relationship

Beyond the above, sometimes it is not possible to keep things simple and, rather than picking very complicated chart types, a combination of charts might be optimal.

6: Get feedback before starting development

Even when taking every precaution in selecting the right charts and graphs, it is still possible to get it wrong. That is why having a failsafe step is vital. The solution? Wireframing.

Whether it is a low-fidelity wireframe such as sketches or using cards and PowerPoint templates or high-fidelity with Adobe XD or a whiteboarding tool, the idea is to get the visualization in a visual form so the audience can give their feedback.

This final step will ensure a tight alignment in picking the right charts and graphs for the intended audience.

KEEP THE CHART AND GRAPH TYPES SIMPLE!

When picking charts and graphs, place simplicity above all is. Is there an easier way to answer a given question? Is it broken down into the smallest parts? Where is the "So what?" and can it be answered more directly?

In data visualization, simplicity is king. That is the reason why limiting the number of charts to select from makes the task of picking the right chart that much easier.

There are three charts that form the basis of the 12 best chart and graph types. Indeed, the additional nine visualizations are simply variations of these foundational three. The foundation charts are:

1. Vertical bar graph or column chart
2. Horizontal bar graph
3. Line graph

By themselves, these charts and graphs should cover many situations and should be considered first. They are widely understood and people know how to interpret them, i.e. keep it simple.

THE 9 VARIATIONS OF THE FOUNDATIONAL 3

However, they can easily be extended and scaled as needed for representing more nuanced data and insights. Together, these 12 charts and graphs cover 90% of use cases when following the steps above.

These 12 primary charts form the basis of picking the right data visualization. While not as simple as the three foundational ones, they offer additional options for displaying and representing context and data with an acceptable reduction in simplicity.

However, what about the remaining 10% of situations where more advanced charts are a must and all attempts have already been made to simplify the message? For those situations, leverage the six secondary chart and graph types. A note here, depending on the domain and industry, there may be a need for greater use of these secondary charts.

The 6 secondary charts and graphs

For special cases, certain industries and domains, there are some more sophisticated data visualizations that need to be called upon. These are the treemap, bubble chart or bubble plot, bubble map, table, scatter plot and a variation of the horizontal bar for population, the butterfly chart.

CHART PATTERNSFOR WHEN ONE CHART IS NOT ENOUGH

EXAMPLE

THE CHART SELECTOR APPROACH

An alternative approach to picking the right chart type is via a chart selector, popularized by Andrew Abela, which breaks charts into several categories. Taking this approach, visualizations can be grouped by how the question needs to be answered. This list is limited to the charts recommended above as other chart types, while can be valuable, have limited application and require additional user training, in general.

BEST CHART AND GRAPH TYPES FOR Comparing Values

• Column bar charts
• Horizontal bar graphs
• Line graphs
• Bubble charts
• Scatter plots

BEST CHART AND GRAPH TYPES FORComposition OF VALUES

• Column bar charts
• Stacked column charts
• 100% stacked column charts
• Horizontal bar graphs
• Stacked horizontal bar graphs
• 100% stacked horizontal bar graphs
• Treemaps

BEST CHART AND GRAPH TYPES FORDISTRIBUTIONS

• Scatter plots
• Bubble charts
• Column bar charts
• Line graphs

BEST CHART AND GRAPH TYPES FOR RELATIONSHIPS ACROSS VALUES

• Scatter plots
• Bubble charts

BEST CHART AND GRAPH TYPES FOR TRENDS OVER TIME

• Line graphs
• Column bar charts
• Area charts

IN SUMMARY, THERE ARE THREE DIFFERENT APPROACHES TO PICKING THE BEST CHART AND GRAPH TYPES

The good news is that there are options, as covering in this post they are:

• Leverage the 12 primary charts and 6 secondary
• Use chart patterns for certain scenarios
• Follow the chart selector approach

Regardless of the framework used, none of them will be truly the "best" until the business context and requirements are properly understood. Check out this video on How to Pick the Right Chart Types.