March 2

12 Best Chart and Graph Types for Actionable Data Visualization


After designing over 500 dashboards for organizations globally, 12 simple charts cover approximately 90% of use cases, resulting in selecting the best chart and graph types for most situations.

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?


  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.


Picking the right chart is not just about data visualization best practice. We must also account for user preference. Are there existing visualizations that they favor? Best practice at the cost of longer term adoption is not a best practice.

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.

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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 of 12 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.


One certain way to lose your audience is in making them think about how to interpret a chart or graph. Don't make them think, make them act!


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.

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The 3 Foundational Charts

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

Column Chart

Best Chart and Graph Types

Horizontal Bar Graph

Best Chart and Graph Types

Line Graph

Best Chart and Graph Types

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.


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.

Column Chart

  • Grouped column chart
  • Stacked column chart
  • 100% stacked column

Horizontal Bar Graph

  • Stacked horizontal bar graph
  • 100% stacked horizontal bar
  • Bullet graph

Line Graph

  • Multiple line graph
  • Dual-axis chart
  • Area chart
12 Best Chart and Graph Types

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.

Secondary Best Chart and Graph Types


Chart patterns take in the wider business context and focus on chart interactions and relationships to get to a sufficient level of detail in order to take action on. It aligns with more human-centric thinking and answering questions rather than considering chart categories.

Chart patterns are more accessible and do not require technical understanding of chart best practices as less emphasis is placed on getting a single chart type exactly right. The point of entry is through business context. For example, imagine this scenario:



By taking typical business scenarios, we can determine what the ideal chart and chart interactions are, embed chart best practices and create a path to action.


Mid-management level employee with historical dependance on spreadsheets and manual data effort.


Supply chain operations for a computer game publisher. Lots of products.


How to I spot any issues in the supply chain across all products and categories?

In this example, what is needed is an operational level perspective for spotting outliers in a supply chain process. There are several other scenarios that this pattern would apply to, of course, but this pattern provides a solid foundation to work from, at least as a starting point.

Data Visualization Chart Patterns


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.


While most types of data visualizations will allow comparison of two or more trends or data sets, there are certain graphs or charts that will make the message all the more powerful.

If the main goal is to show a direct comparison between two or more sets of information, the best choices would be:

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


If the primary aim is to showcase the composition of the data – in other words, show how individual segments of data make up the whole of something – choosing the right types of data visualizations is crucial in preventing the message from becoming lost or diluted.

For parts of the whole and composition questions then consider:

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


When working with a large number of "entities" such as products, customers, employees, distribution charts can understand the range of the measures in the data set as well as trends and patterns.

Effective data visualizations for distributions include:

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


Relationship charts and graphs can can help to identify outliers and assist in highlighting any areas that may need more urgent attention. Though use with caution as these charts can be a bit harder for untrained users to grapple with. 

In these cases, the most effective visualizations include:

  • Scatter plots
  • Bubble charts


Trend charts are useful whenever a value or several values need to be measured over time. Should a question require historical context then a trend visualization is a solid option to consider.

For trends over time, the ideal chart types are:

  • Line graphs
  • Column bar charts
  • Area charts


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.


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Leave a Reply

  1. Finally! Not just another post about the chart selector. Thank you for this different approach, chart selector don’t work for me and this approach matches how I think.

  2. Honestly, the three simple ones are what I use most of the time. I have used the bubble chart a few times and find it good for a lot of situation so why is it not included in the list of 12?

    1. Thank you for your input Deverla and the challenge with bubble charts is that they can be hard for the uninitiated to understand. I love using them too and, when I do, I add a subtitle to aid in the understanding of how it works.

  3. Glad you mentioned about context at the start and actually talking to the end users first. I’ve worked in data viz for many years and most data engineers and BI devs still miss this. I’m sharing this article with the ones I know.

    1. While I don’t have strong feelings about pie charts, many people do and I can understand the rationale behind the sentiment. That being said, when trying to limit ourselves to a select few visualizations, we have to focus on the ones that deliver the biggest band for our visual real estate. The pie chart is just not an optimal way to show data in most cases. However, if adoption is at risk if the user doesn’t get their pie charts, then pie charts it is and we can iterate towards a best practice.

    1. You are indeed correct AstroShooter. We went with the Butterfly chart in this post but could just as well have gone with the negative axis bar chart, which is available in the templates.

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