Types of graphs
There is no single best chart. The right choice depends on what you want to show: a comparison between categories, a trend over time, the distribution of values, parts of a whole, or a relationship between variables. Choosing the right type immediately makes data understandable more quickly.
The wrong chart can actually obscure that same data. Too many categories in a pie chart, an illogical scale, or a shape in which differences are difficult to read can unknowingly steer the interpretation in the wrong direction. Below you will find a practical overview of commonly used chart types, the key differences in terminology, and a quick way to create charts in Excel. You can see how context and scale influence the right choice in Visualizing KPI context.
When do you choose which chart?
When choosing a chart for B2B reporting, too, the choice does not start with the software, but with the question behind the data. If you want to compare categories, a bar chart is often the clearest. If you want to show trend over time, a line chart is the more obvious choice. For distributions, you are more likely to look at a histogram or box plot, and for relationships between two variables, a scatter plot.
The type of data also determines a lot. Is your data categorical or continuous? Do you have few or many categories, one series or multiple? And do you primarily want to make a pattern visible, or do readers need to be able to read exact differences? The more series and categories you add, the faster a graph loses its clarity. Also consider whether a dynamic or static data visualization suits the purpose and the way people use the graph.
Therefore, readability is more important than decoration. Limit the number of colors, add only series that really say something, label clearly, and choose a shape that directly supports the comparison. A simple chart that makes sense immediately almost always communicates better than a striking version that requires extra explanation.
The most commonly used chart types listed
The various types of charts include well-known forms such as bar charts and line graphs, but also box plots, heatmaps, and treemaps. By linking the names of these chart types to the type of insight they provide, you can more quickly identify which choice suits your data; see also data visualization: explanation, examples, and tips.
| Chart type | What does it show? | When does it work strongly? | When is another type often better? | Practical pitfall |
|---|---|---|---|---|
| Bar chart | Compares individual categories or groups based on a single measurement, optionally with multiple series side by side. | Ideal for revenue per product, leads per channel, or error rates per team, especially when exact comparison counts. | It is preferable to use a line graph for time series or a histogram when values are divided into classes. | Too many categories, busy colors, or long labels make it cluttered; sort logically where necessary and keep the number of series limited. |
| Line graph | Show development over time or a continuous series, including accelerations, peaks, and declines. | Strong for monthly revenue, website traffic, sensor data, or other time series where trend recognition is more important than individual moments. | For individual categories, a bar chart is clearer; for the accumulation of totals, an area graph may sometimes be appropriate. | Too many lines in a single image make intersections difficult to read; emphasize only the most important series. |
| Pie chart | Shows how parts together form a whole, usually as a percentage of 100%. | Works if you want to quickly show which part is the largest, medium, or smallest with a few segments. | If you want to compare small differences exactly, it is better to choose a bar chart; it reads more precisely than angles. | More than five or six segments, small differences, or 3D effects quickly make a pie chart misleading. |
| Histogram | Shows the distribution of continuous values in classes, such as ages, wait times, scores, or margins of error. | Suitable for identifying dispersion, skewness, multiple peaks, and gaps in a dataset. | For individual categories, use a bar chart rather than a histogram; for a compact summary, a box plot might be more convenient. | The chosen class division strongly influences the image. Intervals that are too broad or too narrow conceal or exaggerate patterns. |
| Scatter plot | Show the relationship between two numerical variables by plotting each data point as a position on a coordinate system. | Good for correlations, clusters, outliers, and relationships, for example between price and demand or speed and consumption. | If time plays the main role, a line graph is more logical. For a single distribution, a histogram is often sufficient. | Overlap occurs at many points. Transparency, smaller markers, or a regression line can help. |
| Heatmap | Uses color intensity to make patterns visible in a matrix or grid, without centering every value. | Useful for hourly occupancy, performance per segment, daily occupancy, or correlation matrices where patterns are more important than exact readings. | If readers need to read the data precisely, a table or bar chart is often better. For two variables, a scatter plot can be clearer. | Illogical colors, low contrast, or a missing legend make interpretation uncertain, especially with many cells. |
| Boxplot | Summarize a distribution with median, quartiles, spread, and possible outliers. | Useful for quickly comparing multiple groups, for example response times per team or scores per test group. | For a more intuitive view of frequencies, a histogram is often more accessible to a broad audience. | Not everyone reads a box plot correctly immediately; without explanation, quartiles and extremes remain abstract. |
| Area graph | Shows development over time, with emphasis on volume or the breakdown of totals via filled fields. | Can be useful if you want to show how multiple series combine to form a whole and how that whole changes. | For accurate comparison between series, a line graph usually works better because lines are easier to read precisely. | Layered surfaces can hide values or exaggerate differences, especially with many series. |
| Bubble chart | Adds a third variable to a scatter plot via the size of the bubble. | Suitable if you want to show relationship, position, and relative size simultaneously, for example market growth, market share, and margin. | If size is not essential, a simple scatter plot keeps the message simpler and more accurate. | People compare surfaces less precisely than lengths. Misscaled bubbles quickly make differences misleading. |
| Treemap | Show hierarchical divisions using rectangles within rectangles, where size and sometimes color carry meaning. | Useful for categories with subcategories, such as budgets per department, product groups, or storage consumption. | For an exact comparison between parts, a bar chart remains stronger; for a simple parts-whole question, another form may suffice. | Small boxes are difficult to label, and inconsistent colors make the hierarchy unclear. |
Make complex information clear, understandable, and convincing
Graph, diagram, and coordinate system: this is the difference
A graph is a visual representation of data. This can be a line, a series of points, bars, or areas that make information visible. A diagram is broader: it is an collective term for various visual representations, including graphs. So, you could say that a graph is often a type of diagram, but not every diagram is a graph.
A coordinate system is the frame of reference in which many graphs are placed: usually a horizontal and a vertical axis with a scale. The graph itself then consists of the points, lines, or bars that you see within it. To read a graph properly, you always look at the title, axes, scale, units, and legend. For diagrams without axes, such as a pie chart or treemap, labels, ratios, and a legend become particularly important.
This is how you avoid misleading graphs
Use this short checklist before sharing a chart:
- Check the basics first: do the title, legend, labels, and units match what you actually want to show?
- Examine the scale critically. Should the axis start at zero, or is another section justifiable and clearly indicated?
- Use scale breaks only when truly necessary and make them visible so that differences do not appear exaggerated.
- Avoid 3D effects; they distort length, angle, and surface and make comparison more difficult.
- Limit the number of segments in a pie chart and the number of lines in a single line graph.
- Check bubble sizes and color usage: both can make a pattern appear stronger or weaker than it is.
- Do you notice that a comparison, trend, or distribution is not immediately readable? Then switch chart types or choose an infographic or dashboard instead of further embellishing the current format.
Create charts quickly in Excel
The basic order in Excel is simple:
- First, organize your data neatly into a table, with clear column headers and no empty rows within the range.
- Select the data you want to visualize and choose Insert and then a chart type, or start with Recommended charts.
- Check whether the proposed type matches your question in terms of content: comparison, trend, distribution, composition, or relationship.
- Only then adjust the title, legend, data labels, colors, and scale so that the graph remains readable and fair.
- The menus may differ slightly depending on the Excel version, but this method remains broadly the same.
How we choose the right visualization for complex information
With complex information, data is rarely the whole story. The same figures require a different format if you want to help a marketing team compare, support an executive team with data visualizations for board decision-making, or explain a technical operation step by step. That is why we look not only at the data point, but also at the message, target audience, and context. With the right chart types, you build data storytelling that aligns with the goal and the decisions you want to support.
In over 10 years and across more than 3,000 animations and visualizations, we have seen that a single graph is sometimes sufficient, but far from always. With infographics, process and instructional infographics, data visualizations, and explainer animations, additional context often proves decisive: sequence, emphasis, and sometimes movement make it clear what the viewer needs to understand or remember. The best visualization is therefore not only accurate but also strategically chosen within the larger story.
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