Choosing the Right Chart: A Personal Guide to Better Data Visualization

Why Choosing the Right Visualization Matters

I’ve been visualizing data for quite a few years now, and if there’s one thing I’ve learned, it’s that choosing the right chart can make or break your message. I’ve spent countless hours reading, learning, and experimenting — immersing myself in works by data visualization experts like Leland Wilkinson, Cole Nussbaumer Knaflic, and many others.

I’ve seen charts that tell a story with elegance and precision, and I’ve seen ones that muddle the message so badly you’d wish you had just stuck with a table. There’s a lot of advice out there on what chart to use when, but it often feels disconnected, like a bunch of rules with no clear path.

That’s why I’ve put together this guide — not just as a list of dos and don’ts but as a structured approach that you can follow to choose the right visualization for your specific needs. My goal is to help you make sense of your data, your purpose, and your audience so that you can communicate your insights effectively.

Step 1: Formulate the Main Question of Analysis

Before you dive into charting, take a step back and ask yourself: What’s the main question I need to answer with this data? It’s easy to get caught up in the excitement of plotting data without a clear purpose. But without a guiding question, your chart can end up as noise rather than a tool for communication.

Think of the main question as the anchor for your entire visualization process. Are you trying to understand trends, compare categories, or reveal relationships? For example:

• “What are the monthly sales trends for our products over the past year?” suggests a need to show time series data.
• “How do our top three product segments compare in sales performance?” hints at a comparison.
• “What is the relationship between marketing spend and sales growth?” points towards understanding correlations.

Personal Tip: When I’m working on a visualization, I always start by writing down the main question as if I’m explaining it to a colleague. It forces me to clarify my thoughts and makes sure I’m not just plotting data for the sake of it.

Step 2: Ideate Auxiliary Questions

Once you’ve nailed down your main question, it’s time to dig a bit deeper. Break that big question down into smaller, more manageable parts — what I call auxiliary questions. These are the questions that will guide you in understanding the context and details of your data.

For instance, let’s say your main question is about monthly sales trends. Auxiliary questions might include:

• “Are there particular months where sales spike significantly?”
• “Do certain product categories drive most of these sales peaks?”
• “Are there identifiable patterns, like seasonality, in the data?”

These auxiliary questions help you identify key aspects of your data that need attention. They often lead you toward more specific visualizations that are tailored to these nuances rather than a one-size-fits-all approach.

Personal Insight: I’ve found that auxiliary questions are like a compass; they steer the direction of your analysis and visualization. Early in my career, I would often skip this step, thinking the main question was enough. Big mistake! Breaking down the problem helps uncover hidden insights that can completely change the narrative.

Step 3: Look for Specific Expressions in Questions to Choose the Type of Representation

Now comes a critical step: translating those questions into visual choices. To help with this, I’ve created a Table of Keywords Pointing to Visualization Types. This tool acts as a bridge between the language of your analysis and the world of charts. It maps common phrases and keywords from your questions to suitable visualization options.

Here’s how it works:

• If your question includes “compare categories,” it steers you toward bar charts, column charts, or dot plots.
• If you see “show trends over time,” you’re likely looking at line charts, area charts, or time-series plots.
• For expressions like “distribution” or “spread,” histograms, box plots, or violin plots are your go-to options.

Advice from the Field: Over time, I’ve kept a personal list of these keyword-to-chart mappings, and it’s saved me countless hours of second-guessing. You don’t have to memorize every chart type — just match the language of your data question to these visual cues.

Examples of Using the Table of Keywords:

Imagine you’re working on a project to analyze customer satisfaction survey data. Your main question is, “What is the overall distribution of satisfaction scores among customers?” Using the table, you see keywords like “distribution,” pointing you to options like histograms, box plots, or density plots. But if you dig deeper and ask an auxiliary question — “How does satisfaction differ across age groups?” — the table might direct you to a grouped box plot or violin plot that visualizes distribution across categories.

Step 4: Specify the Number of Dimensions Needed to Visualize

Understanding the complexity of your data is crucial in choosing the right chart. When I talk about dimensions, I’m referring to how many layers of information you need to show. A simple line chart might work for one variable over time, but what if you want to compare multiple variables?

Here’s a breakdown:

• One Dimension: Single-variable charts like line charts for trends, or bar charts for category comparisons.
• Two Dimensions: Scatter plots for relationships between two variables, or grouped bar charts to compare multiple categories side by side.
• Three or More Dimensions: More complex visuals like bubble charts, 3D scatter plots, or even faceted grids that show multiple charts in a single view.

Field Experience: The more dimensions you add, the trickier it gets. One mistake I see often is overloading a chart with too much information, making it incomprehensible. When in doubt, keep it simple. You can always provide additional views or drill-downs.

Step 5: Get Visualization Sets Based on Points 3 and 4

Now that you’ve identified potential chart types from your keywords and understood the dimensional needs of your data, you can start pulling together a set of possible visualizations. This is where the Table of Visualization Types by Concept comes in handy.

This table doesn’t tell you what the best chart is; it shows you what charts can be used based on your analysis needs. It’s not about narrowing down to one immediately — it’s about seeing all your options.

Example:

If you need to show a comparison, the table will list out bar charts, dot plots, radar charts, and even more advanced options like slope charts or dumbbell plots. If you’re focusing on relationships, you’ll find scatter plots, bubble charts, and network diagrams as potential candidates.

Personal Tip: I often treat this step as a brainstorming session. I’ll sketch out a couple of chart types on paper or in a tool just to see how the data feels in different forms. Sometimes a chart I didn’t initially consider turns out to be the most effective.

Step 6: Classify Visualizations According to Your Audience

A crucial lesson I’ve learned is that not every chart is suitable for every audience. Over the years, I’ve seen beautifully complex charts fall flat in presentations because they simply went over the audience’s head. This is why I’ve classified visualizations into three main categories in the Visualization Classification Table:

1. Avoid Anyway: These are the troublemakers — charts that often mislead or confuse, like 3D bar charts or pie charts with too many slices. Even experienced audiences can struggle with these.
2. Use Only for Data Literate/Technical Audience: Charts like heatmaps, violin plots, or parallel coordinates plots are fantastic for deep analysis but require a certain level of data literacy to interpret correctly.
3. Always Good: These are your safe bets — line charts, bar charts, scatter plots. They are reliable, intuitive, and communicate effectively to most audiences.

Advice from My Journey: This classification system is a game-changer. Knowing what to avoid and what your audience can handle takes your visualization game to the next level. Early in my career, I used a radar chart for a presentation about customer profile, only to realize halfway through that no one could understand it. Since then, I’ve been far more selective about matching charts to the right audience.

Step 7: Choose One Type and Return to Questions to Customize Chart

Finally, after narrowing down your options, it’s time to make a choice. But don’t stop there. Return to your main and auxiliary questions to see if there are specific details that need emphasis. This is where customization comes into play — annotations, data markers, color schemes, and axis labels can all be adjusted to highlight key insights.

Customization Tips:

• Use Color Wisely: Highlight key data points or trends without overloading the viewer’s senses.
• Annotations Matter: Adding text to call out critical points can help guide your audience through the data story.
• Interactive Elements: If your audience is engaged with dashboards, interactive elements like tooltips or filters can add depth to the visualization.

Experience Insight: The finishing touches can elevate a basic chart into an engaging story. I’ve seen simple line charts transform with just a few well-placed annotations or by tweaking colors to emphasize critical trends.

Conclusion: Your Checklist for Choosing the Right Chart

As you work through the framework, keep a checklist handy to ensure you’ve covered all bases:

• Have you clearly defined your main and auxiliary questions?
• Are you matching keywords to visualization types appropriately?
• Have you considered the complexity of your data dimensions?
• Did you review all possible visualizations before making a selection?
• Are you tailoring the visualization to your audience’s data literacy?
• Have you customized your final choice to best represent your insights?

Choosing the right chart is as much an art as it is a science. By following this structured approach, you can navigate the vast landscape of data visualization with confidence, ensuring that your charts aren’t just visually appealing but also effective in communicating your message.

Extras

Table of keywords — how your business question point type of chart

https://github.com/kgryczan/medium_publishing/blob/main/Keyword%20table.pdf

Visualization Classification Table

https://github.com/kgryczan/medium_publishing/blob/main/Audience%20qualification.pdf

• Avoid Anyway: These are charts that often distort data, are visually confusing, or are commonly misinterpreted. They should be avoided unless you have a specific, justified reason to use them.
• Use Only for Data Literate/Technical Audience: These visualizations provide deep insights but require a certain level of data literacy to interpret correctly. They are best suited for audiences familiar with data analysis.
• Always Good: Reliable, intuitive charts that work well for most audiences and effectively communicate key insights. These are your go-to options when clarity and simplicity are essential.

Table of Visualization Types by Concept

https://github.com/kgryczan/medium_publishing/blob/main/Vizzes%20by%20Concept.pdf

Thank you for taking the time to read through this guide! I hope it helps you make more informed decisions about your data visualizations. I’m always eager to hear your feedback, so feel free to connect with me on LinkedIn and let me know your thoughts on the tools and framework I’ve shared. Your insights and comments are invaluable as I continue refining these resources.

I apologize if the tables aren’t designed to the perfect DTP (desktop publishing) standards — I’m all about the content and making sure the information is useful, even if it’s not wrapped up in the prettiest package.

Stay tuned, because I’ll be publishing more content about data visualization on LinkedIn, diving deeper into reverse engineering this framework. I’ll be breaking down specific charts: how they look, when they work best, how they’re classified for different audiences, and their potential business applications, along with the pros, cons, and common pitfalls of each. Let’s keep the conversation going, and I look forward to sharing more with you soon!

Choosing the Right Chart: A Personal Guide to Better Data Visualization was originally published in Numbers around us on Medium, where people are continuing the conversation by highlighting and responding to this story.