Exploratory vs. Explanatory Charts: What’s the Difference?
Charts are one of the most powerful tools we have for making sense of data. But not all charts serve the same purpose. Some are meant to help you, the analyst, dig into the data and uncover patterns. Others are designed to help your audience understand your conclusions quickly and clearly.
This distinction—between exploratory and explanatory charts—is one of the most important in data visualization. Knowing when to use each type can sharpen your analysis and make your communication more effective.
What Are Exploratory Charts?
Exploratory charts are the visuals you create during the process of analysing data. Their purpose is discovery. You’re not worried about how polished they look—you’re trying to figure out what the data is telling you.
Think of exploratory charts as a conversation between you and your dataset. You might generate dozens of these visuals as you test hypotheses, filter out noise, and look for trends. They’re quick, iterative, and often messy.
Examples of exploratory charts include:
A scatter plot testing whether two variables are correlated.
A histogram showing how data is distributed.
A line chart plotting raw time-series data before smoothing.
Exploratory visuals are for you. They don’t need to be elegant, but they should help you ask better questions.
What Are Explanatory Charts?
Explanatory charts are built for communication. Once you’ve explored your dataset and identified the story you want to tell, you shift gears into explanation.
These charts are cleaner, more polished, and more intentional in design. Every element—colour, labelling, annotation—is chosen to make your key message obvious to the audience.
Examples of explanatory charts include:
A bar chart showing year-over-year growth in sales.
A simplified line chart highlighting a turning point in a trend.
A single pie chart showing proportions that matter in context.
The goal of explanatory charts is persuasion and clarity. They’re designed for presentations, reports, and dashboards where the focus is on communicating the “so what.”
For inspiration, the New York Times Graphics section showcases some of the best explanatory charts in practice.
Key differences at a glance
| Feature | Exploratory Charts | Explanatory Charts |
|---|---|---|
| Purpose | Discovery and analysis; used to find patterns, test hypotheses, and understand the data. | Communication and persuasion; used to convey the key takeaway clearly to others. |
| Audience | Primarily the analyst or internal team doing the work. | Stakeholders, decision-makers, or public readers. |
| Design | Rough, iterative, possibly messy; speed over polish. | Clean, polished, intentional; every element supports the message. |
| Number of Charts | Many visuals during analysis; rapid iterations. | Few visuals; one or two charts to make the point. |
| Examples | Scatter plots to test correlations; histograms to inspect distributions; raw time-series plots. | Bar chart of year-over-year growth; simplified line chart highlighting a turning point; annotated single-metric chart. |
| Annotations & Labels | Minimal; labels often default or temporary. | Prominent; direct labels and annotations guide the reader. |
| Consistency of Scales | Flexible; may change scales during probing. | Consistent and transparent; scales chosen to avoid confusion. |
| Outcome | Insights and hypotheses to validate. | A clear takeaway and recommended action. |
Why the Distinction Matters
Confusing exploratory with explanatory charts is one of the most common mistakes in data communication. For example, an analyst might share a cluttered exploratory scatter plot with an executive team. While the chart may have helped uncover an insight, it won’t resonate with an audience unfamiliar with the raw data.
On the other hand, if you jump straight to explanation without exploring, you risk missing important patterns or nuances. Both phases are essential, but they require different mindsets.
Bridging the Gap
Here’s how to move effectively from exploration to explanation:
Explore broadly. Generate multiple visuals to test your assumptions. Don’t worry about polish at this stage.
Identify the story. Once you’ve uncovered an insight, step back and ask: What is the one message my audience needs to hear?
Refine for clarity. Redesign the chart with a focus on simplicity and storytelling. Remove clutter, highlight the key data, and add context.
Tailor to your audience. Executives may prefer a single clear chart; technical teams may appreciate a bit more detail.
For a practical example of this transition, Cole Nussbaumer Knaflic’s Storytelling with Data blog often shows how a raw exploratory chart can be redesigned into a polished explanatory visual.
Final Thoughts
Exploratory and explanatory charts aren’t in competition—they’re two sides of the same coin. Exploration helps you find meaning in data, while explanation helps others see and act on that meaning.
As a non-analyst, the takeaway is simple:
Use exploratory charts to learn.
Use explanatory charts to teach.
When you separate the two and give each its due, your data stories will be clearer, more persuasive, and far more impactful.
