Annotations #

It is rare that data speaks entirely for itself. Data alone can show a peak, but it can’t tell you “New product launched.” A line can show a dip, but it can’t say “Market crash.”

Annotations are the essential storytelling layer of your chart. They are not a “last resort”; they are a primary tool you use to guide the reader’s attention and provide interpretation. A well-designed annotation turns a simple plot into a compelling argument.

Let’s look at the three most common “jobs” an annotation can do.

1. Spotlighting: “Look Here!” #

The most common job is simply drawing attention to a specific data point. The simplest tools for this are symbols like an asterisk (*) or an arrow (→). Additionally, asterisks are often used to indicate a statistically significant difference between two measurements, and will be found on bar charts indicating differences between bars, itself an annotation!

When designing these, consider their color. By default, an annotation might be black, which visually associates it with the axes and frame. A more powerful choice is to match the annotation’s color to the data it describes. This creates a clear, intentional link between the data and your commentary on it.

You can even combine this with the ideas discussed in the article on consistency and contrast to make the data point itself stand out.

2. Interpreting: “This is what this means.” #

The next step is to add explanatory text. This can range from a simple ($x$, $y$) value to a full sentence. When adding text, you must integrate it using proximity and color.

As you add more text, you’ll run into challenges. A long, left-aligned block of text can look messy when an arrow is pointing to it, so you can use center alignment for the text.

But this can still lead to a situation where the text appears to be floating or disconnected from the chart. Our next point will consider how to address this problem.

3. Contextualizing: “This whole area is important.” #

Sometimes you want to annotate a region of data, not a single point. For this, enclosure is your best tool. By placing a shaded box around a region, you can draw attention to it and label it.

This technique also solves our text-labeling problem. By enclosing your text in a box, you “unitize” it. This gives you a clean, simple shape to connect with an arrow, which makes the text feel far more “entrenched” within the plot. Rather than a floating block of text, it feels like a single unit of annotation.

Conclusions: Integrate, Don’t Just Add #

The goal of an annotation is to add information without adding clutter.

To do this, you must treat your annotations as integrated design elements, not as an afterthought. Use the principles you already know:

• Use consistency and contrast to connect an annotation to its data.
• Use proximity and separation to place labels close to their subject.
• Use enclosure to group text into a clean, single unit.

A well-designed annotation doesn’t compete with the data; it completes it.