Chart Design Tips Every Data Analyst Should Know
When it comes to making data visualizations that actually resonate, few people have influenced the field as much as Cole Nussbaumer Knaflic, author of Storytelling with Data. Her philosophy is simple but powerful: good charts are not about fancy visuals but clarity, intention, and storytelling.
Whether you’re a new data analyst or want your next presentation to land better, these ten principles inspired by Knaflic’s work will help you make more effective and insightful charts.
1. Start with the context
Every great chart begins with a question: Who is the audience, and what do I want them to take away?
The context drives everything—what data to show, what to leave out, and how to frame your visual. A chart that works in an internal analysis report might not work in an executive presentation. Before you open Excel, Sheets, or Python, define your message.
2. Choose the right visual
Not all charts tell the same story.
Use line charts for trends over time.
Use bar charts for categorical comparisons.
Use scatter plots to show relationships between variables.
Knaflic reminds us to avoid unnecessary complexity. A clear bar chart will always outperform a 3D pie chart in conveying insight.
3. Eliminate clutter
Clutter is the enemy of comprehension. Remove gridlines, borders, shadows, and redundant labels. Reduce tick marks where possible. Every element on the page should serve a purpose. If it doesn’t help tell your story, it’s a distraction.
4. Use color strategically
Color is one of your most powerful storytelling tools—but it should be intentional, not decorative.
Use neutral tones for context and bold color for emphasis.
Limit your palette to just a few hues.
Avoid red-green contrasts that are problematic for colorblind readers.
Color should direct attention, not compete for it.
5. Emphasize what matters
Highlight your takeaway visually.
Make key bars or lines darker or brighter.
Add annotations or data labels to reinforce the main point.
Fade or gray out less relevant data for context.
If the audience’s eye lands immediately on your main insight, you’ve done it right.
6. Make text work for you
Don’t rely on your audience to figure it out—tell them what to see. Use direct labels instead of legends, and write titles that communicate meaning, not just describe the chart. “Profits rose 25% in 2024” communicates more than “Profit Over Time.” Text is part of your design—make it do some of the storytelling.
7. Think like a designer
Even if you’re not a designer, a few visual design principles go a long way. Use alignment, proximity, and white space to organize information. Create a visual hierarchy so that the most important elements stand out first. Good design doesn’t have to be flashy—it just needs to guide the viewer’s eye smoothly.
8. Tell a story
Every effective visualization fits into a narrative arc:
Set the context.
Show the data clearly.
Reveal the insight.
End with action or implication.
Your chart should help the audience connect the dots and understand why it matters. The goal isn’t just to inform—it’s to inspire understanding or change.
9. Iterate and test
Your first draft won’t be your best. Try different layouts, colors, and chart types. Ask a colleague who hasn’t seen the data to interpret it—can they grasp the key takeaway in five seconds? If not, simplify and clarify until they can.
10. Practice restraint
Knaflic often emphasizes one rule above all: show only what matters. The more you add, the harder it is for your audience to focus. Great charts are not about everything—they’re about something.
| Principle | Key Takeaway |
|---|---|
| 1. Start with context | Know your audience and the purpose before you design. |
| 2. Choose the right visual | Match chart type (line, bar, scatter) to the message you want to convey. |
| 3. Eliminate clutter | Remove unnecessary elements so the data stands out. |
| 4. Use color strategically | Use neutral tones for context and bold color only for emphasis; consider accessibility. |
| 5. Emphasize what matters | Highlight the key takeaway with contrast, annotations, or direct labels. |
| 6. Make text work for you | Use clear titles and direct labels that communicate the insight. |
| 7. Think like a designer | Apply visual hierarchy, alignment, and white space to guide the eye. |
| 8. Tell a story | Structure charts as a narrative: context → data → insight → implication. |
| 9. Iterate and test | Refine charts through feedback until the main point is clear in seconds. |
| 10. Practice restraint | Show only what’s essential so the audience can focus on the insight. |
Tools to Try
Want to put these lessons into practice? Here are a few accessible tools that make it easy to experiment with data storytelling:
| Tool | Best For | Why Try It |
|---|---|---|
| Google Sheets | Everyday charts and dashboards | Simple, shareable, and excellent for learning basics like color, labels, and emphasis. |
| Flourish | Interactive, web-ready visualizations | Build scrollable or animated visuals with minimal coding—great for storytelling. |
| Datawrapper | Clean charts and maps for publication | Designed for readability; quick to publish embeddable charts and maps that follow good design defaults. |
| Tableau Public | Deeper interactivity and exploration | Powerful for exploring relationships and building interactive dashboards for complex datasets. |
| Canva | Polished static visuals | Quickly combine charts, headlines, and annotations into presentation-ready graphics. |
| Python (matplotlib / seaborn) | Programmatic control and reproducibility | Offers precision, automation, and full control over design for analysts who code. |
Final Thoughts
Cole Nussbaumer Knaflic’s approach to data storytelling reminds us that effective charts are not about decoration—they’re about communication. Every visual choice should make your message clearer and your insight stronger.
Next time you build a chart, start by asking: What do I want my audience to see, feel, and remember?
If your design answers that clearly, your data is doing its job.
