Hi,

• Go-to chart for storytelling: I often lean towards scatter plots when I want to show relationships or correlations between two variables, especially when the density of points or clusters tells a story. For showing trends over time, a well-executed line chart is hard to beat for its clarity. And for comparisons across categories, a straightforward bar chart (horizontal often feels more readable for longer labels) usually does the trick. The “why” always comes back to what story I’m trying to highlight.

• Balancing aesthetics vs. clarity: Clarity always wins in my book. A beautiful but confusing visualization misses the entire point. I aim for a clean and intuitive design first. Once the message is crystal clear, I might then consider subtle aesthetic enhancements like color palettes that align with the data or branding, thoughtful typography, and clean layouts. It’s a delicate balance – the aesthetics should support the clarity, not compete with it.

• Misleading beautiful visualizations: Oh yes, absolutely! I’ve seen some stunning visualizations that, upon closer inspection, used deceptive scaling on axes or cherry-picked data points to create a narrative that wasn’t entirely accurate. Sometimes it’s unintentional, but other times… well, let’s just say it highlights the ethical responsibility that comes with data visualization. I’ve definitely caught myself making a chart look “better” in a way that slightly skewed the perception, and it’s a good reminder to always double-check the integrity of the visual representation.

• Favorite example of a well-done dashboard/graphic: There are so many great ones out there! I’m consistently impressed by dashboards that seamlessly integrate different chart types to provide a holistic view of complex data while still being easy to navigate. For individual graphics, I often admire those that use annotations and thoughtful layering to draw the viewer’s eye to key insights without being overwhelming. Information is Beautiful’s work by David McCandless often strikes this balance beautifully.

Some general tips I’ve picked up:

• Know your audience: Tailor your visualizations to their level of data literacy and what they need to get out of the information.
• Less is often more: Avoid clutter. Remove unnecessary elements like excessive gridlines, labels, or distracting backgrounds.
• Color with intention: Use color strategically to highlight key data points or categories, and be mindful of accessibility (colorblind-friendly palettes are crucial).
• Tell a clear story: Every visualization should have a purpose. What message are you trying to convey?