Data visualization is a great tool for presenting complex data in a simple way. It is better to see something once than hear about it a hundred times. But that power comes at a price. Mistakes can make data visualization misleading. The harm of these mistakes can cross out all the advantages of data visualization.

In this article we’ll walk you through all possible mistakes and bad visualizations. We’ll show you how to avoid them and talk about alternative good practices.

Interested? Keep on reading!

Bad Data Visualization Explained

How can we spot bad data vizualization? It misinforms or misleads the viewer by the misusage of data visualization principles.

Things can go wrong on several levels:

Bad data (limited data) can mean a few things. First, it can mean that there is not enough data for the data visualization to see the context. Also, there can be some crucial data missing. In this case, data visualization can hardly do the job. You need to collect full data or show that certain data is missing. Bad data can be also in the form of missing text notes, comments, or titles.

Bad chart choice happens when you use a chart type that is not designed to show a certain type of data. In most cases you can fix it using another chart type that fits the purpose.

Bad chart usage is connected with overuse or misuse of certain elements or attributes of a chart. There can be too many elements, too many axes or too many embellishments.

Now let’s walk through some detailed examples. It’s getting practical. Keep on reading.

Misleading data visualizations because of data handling

Data is too raw

There are different ways to use data visualization. You can use it for internal purposes, research, or to get acquainted with the data. It’s easier to explore data via a chart than a data table. You can see relations, trends, clusters, etc. But here we’ll talk about presenting data by visualizing it. In this case, presenting the whole dataset is a bad way to approach visualization.

An appropriate way is to experiment first. When experimenting, you can try different charts and views. But when you present data, you need to know what you want to share and the things you want to highlight. So use clean and filtered data for final visualisations.

Incomplete data

It’s always a bad idea to visualize data that is incomplete. And even worse to do it on purpose. There’s a grey area when you know the data is incomplete, but the other part of the data is so important that you need to show it in some way.

In this case, the various visual elements come to help. The data that is incomplete should be highlighted with the other visual elements. An example is using dotted lines in a line chart. When values are missed in a certain range, a dotted line is used to connect the known values to maintain the unity of the line. A break in the line or using data marks can also be an option. It depends on the context of usage.

Data and visualization show different things

It is a strange point, but it happens surprisingly often. In most cases, it is done on purpose to highlight something that doesn’t exist. It also happens not on purpose when some non-specific tools are used for data visualization or approximate values are used.

The only way you can avoid that is to be truthful and double-check the visuals you’re making.

Misleading data visualizations because of chart choice

Wrong chart function

This is a point that relates to bad chart choice in general. There are several chart groups. Each group is based on a certain type of data. They are: comparison charts, time series charts, part-to-whole charts, correlation charts, distribution charts, flow charts, geospatial charts/maps.

To choose the right chart type, first you need to understand the aim of the visualization. Then check the structure of your data and then choose a chart from an appropriate group. In the article about 80 types of charts you can find a list of chart types divided into mentioned groups.

Pie chart: using for comparison

Pie charts are a bad choice for visualizing comparison. It is so because of the visual specificity of pie charts. Sectors are harder to compare, so the small differences might be not even visible on pie charts.

The solution for this case is simple. Use a bar chart instead. It allows us to see even the tiniest differences between values.

Bar chart: using for time series

A bar chart is not a good chart for time series. It gets worse if a grouped bar chart is used and every timestamp is represented by several bars. To visualize time series you need a lot of data points in most of the cases. The bar chart makes it too cluttered. The strict condition of starting bar charts from zero limits the possibilities of bar chart usage for showing trends. Yet for the cases where the number of data points is low, bar charts can work.

The solution is simple — use line charts for time series if you want to see trends. If there are only a few data points — it is safe to use a bar chart.

Treemap or Icon chart: use for comparison of values of the same order

Treemaps or icon charts can be used for comparison. For a specific case when the values differ a lot, like 10 and 10000, it is a good solution. In this case, both of these charts work well. But for the case when the values are of the same order, treemap and icon charts are not the perfect choice for comparison. Viewers generally read the size of areas worse than the length of a bar. And also the areas representing values of the same order will look similar which also makes it harder to compare.

The solution is simple. Use a bar chart for or comparison of values of the same order.

Misleading data viualization because of bad chart usage

3d charts

There’s no doubt 3D charts can look flashy and eye-catching, but it is rare to find one that communicates important information clearly. More often than not, 3D charts distort proportions, making it complicated to compare the categories. Moreover, they are hard to read, with the categories often too far apart to draw any accurate comparison.

The solution is simple. Don’t use 3D charts, use the plain version.

Double axis

It’s not a rare thing that you see a double axis on different charts, but it’s a very disturbing practice. The double axis confuses the reader. Is the line or bar related to the left or right axis? It is always a puzzle.

The main issue of a double axis is that the scale of the left and right axes are usually different and show values that are not related. They often have even different units of measurement.

How to fix it? Use two separate charts. If you want to show the correlation between two values — use scatter plots.

Wrong placement of charts with different scales

In the previous point, we told you about the necessity of making two charts instead of one with double axes. But there’s an extra note. You shouldn’t place two charts side by side in a way that the reader might consider these charts to have the same scale or same axis.

The optimal placement of charts is on top of each other if they share the horizontal axis (usually its X axis) and side by side if they have the same vertical axis (Y axis).

Too many elements

Sometimes the amount of data is overwhelming and there’s an urge to place all the data on one chart. For example, scatter plots often have extended possibilities of binding multiple variables. You can assign variables to different attributes of the chart, like position, shape, color, and size. But it doesn’t mean you should use all of them. It will just create more confusion.

To avoid this situation you need to decide first what message your data should deliver and which part of the data you want to display. Usually, you’ll need only certain variables. Of course, there are cases when you need to provide all the data to show the context. Then you can use grey color for all the data and highlight certain chart elements with color.

No context (no scale, no axis, no labels)

Data visualization is obviously about presenting data with visual elements, but it doesn’t mean that numbers and labels are not allowed. Axes, and labels are necessary to provide the context and make the chart meaningful.

Bar chart: starting not from zero.

The first rule of data visualization: always start the bar chart from zero. It is important because of the visual essence of the bar chart. We often perceive bar charts only by the height of the bar but actually the area of the chart represents the value. So if we start the bar chart not from zero we fail to show the whole value.

If the bar chart doesn’t work for you because you need to show data in a specific range — you can use a line chart or dot plot instead. For these charts, it’s okay to use a relative scale on the Y-axis. Just make sure the range you choose stays close to the actual data values.

Bar chart: multicolored bars

Multicolored bar charts are considered bad because categorical coloring doesn’t add any information. It just adds confusion.

The best way to color bar charts is to color them in one color and highlight one or more bars to accent certain details.

Bar chart: truncated Y-Axis

In bar charts where one or more values have a different scale than the other bars, truncation of Y-Axis is often used. It’s not a good solution as the truncation makes the size of the large bars smaller. It distorts the perception of the data.

A solution for this case is to use an icon chart instead of a bar chart. The icon chart uses the area of the chart as a representation of the value. It allows us to show data in a more compact way. Also, a treemap might be considered as an alternative.

Bar chart: complex-shaped icons are used as a substitution for bars

Icons are widely used in data visualization. They are the essential part of the ISOTYPE visualization system. Yet some designers overuse icons in an unnatural way — as bars for bar charts. As mentioned above, bar charts are perceived not only by their height, but also by their area. When the icons are used they tend to be increased in size in both dimensions — which is wrong. The other thing that backfires is the complex shape of these figures. It is already a non-trivial task for a reader to perceive the area of the figure. If it is a complex shape — it makes it almost impossible to read this kind of chart.

An alternative solution in this case is to use a regular bar chart. If there’s an absolute necessity to use icons they can be used as an addition to categorical labels. If the data should be represented by icons, you should choose an icon array as a chart type. It allows you to represent a certain base value with the icon. Multiple icons in this case represent a value from a dataset based on the base value of one icon.

Grouped bar chart: using too many series

It’s a common situation when grouped bar charts are used to show many items under one category. The problem here is that this kind of grouped bar chart is hard to read. If you use many series you will need a legend and then when you go back and forth between the chart and the legend you can get a vertigo.

The recommendation here is to use only a few series for grouped bar charts — less than three, preferably. An alternative option is using a line chart or dot plot. It will look cleaner.

Column chart: angled labels

The configuration of the column chart makes it difficult to place a wide categorical label. The base of the bar chart is narrow and wide labels tend to overlap. A popular, but not user-friendly solution, is to change the angle of the labels to 45 or 90 degrees. In this case the reader needs to tilt the head to read the labels, which can even cause neck strain:) No one wants that.

The solution is to use a bar chart instead. The direction of bars allows the placement of almost any category label.

Line chart: spaghetti chart

Spaghetti chart is a nickname for a line chart with lots of multi-colored lines. It is considered a bad visualization as it is hard to follow each separate line.

The solution is to create separate charts for each line or groups of lines (no more than 4 lines per chart). If you need all the lines in one chart for the context — color them in gray and highlight the line that you want to focus on.

Line chart: cumulative chart

Cumulative charts are not bad charts per se, yet they are overused because they are moving upwards into the top right corner, which looks like a positive trend for the reader.

Good practice in this case would be using a line chart that shows the changes for every period.

Line chart: wrong scale

Choosing a scale for a line chart is a subtle balance between showing the context of the value and showing the changes of the value in a full and clear way. There are two opposites of this case. The first case is when you have a very large scale and you can barely see the changes on the line. It looks more like a flat line. The second case is when you have a chart that shows every small detail of the line, but no context is shown. It might be very important if you are showing a few line charts for different cases.

A solution is to show both the context and the details of the line. The extreme case is when you have one chart for the context and one chart for details, like a zoom-in version.

Line chart for discrete data

This type of bad data visualization falls into a greater category “wrong chart for certain data type”. The function of a line chart is to visualize continuous data, so using a line chart for discrete data is both strange and wrong.

An alternative would be any chart that can work with discrete data, for example a bar chart.

Area chart: starting not from zero

The situation here is like what we have with bar charts. If we start area charts not from zero we disturb the perception of the area below the chart.

The recommendation is to start the area chart from zero. As an alternative, you can also use line charts.

Pie chart: too many sectors

The specific feature of pie charts is that it is using sectors for showing the values. The way the sectors can be perceived is by angle or by the area of the sector. Both are not trivial for reader perception. It works good only in cases where the number of sectors is less than 5 and the difference between sectors is obvious.

In this case, we would recommend using the “rest” or “other” category. Categories that have a corresponding value that is less than a certain threshold can be placed under the “rest” category.

Pie chart: percentages do not add up to 100%

It’s one of the most popular mistakes when using pie charts. The sum of the sectors should always give exactly 100%, not less, not more. Also, you should be aware of that rule when you’re working with absolute values in the context of part-of-whole relationships. There is also a case when the percentages do not add up to 100% because of the rounding issues.

For the cases when the sum of slices don’t give 100% because of the rounding you can still use pie charts. You just need to fix the rounding. If you have values that don’t give 100% in sum because it shouldn’t be 100%, use another chart, like a bar chart.

Nightingale chart: using the chart casually

The Nightingale chart is a very important point in the history of data visualization. It is one of the examples when a chart made a big difference in the real world. However, it is a very tricky chart to use, both for the creator and the reader. The proper way to show values using this chart is via sector angle and area of the sector. But really often the radius of the sector is used instead of the area to depict the value, which is wrong and misleading.

The advice here is not to use this chart at all. The difference is the way it can be read and interpreted. It might create a lot of confusion. Better to use other part-to-whole charts like stacked bar charts, regular pie charts, or a treemap. For showing the second dimension, it’s better to use an additional chart.

Icon chart (bubble chart): using diameter (circles) or height of a figure (squares) for visualizing values

Another popular mistake is using the wrong parameter of an icon instead of the area. For icon charts or bubble charts, the values are visualized via the area of the figure. No other parameter can be used.

The advice here is pretty clear — use only areas to visualize values for icon-based charts.

Conclusion

Data visualization is a powerful tool. But with great power comes great responsibility. As we’ve seen throughout this article, poorly designed charts can distort facts, confuse audiences, and ultimately mislead decision-making. From using the wrong chart type to cluttering visuals with unnecessary elements, bad practices can undermine even the most accurate data.

The good news is that these mistakes are avoidable. By understanding the purpose of your visualization, choosing the right chart, simplifying where necessary, and always prioritizing clarity and honesty, you can ensure your visuals serve their intended goal: to inform, not misinform. In the end, good data visualization is not just about making charts look appealing. It’s about making data meaningful.

Originally published at https://www.datylon.com.

Recently we’ve rewritten our essential article about 80 types of charts. Our main goal was to make the information structured and helpful right away. We talk about every chart starting from the look and feel and then going into practical advice on when you should and when you shouldn’t use this particular chart type. We are publishing parts of this article here. One chart group per each article.

In the sixth part, we’ll talk about geospatial visualization and other graphs types. Keep on reading!

Geographic heatmap

Alternative names: hot spot map, geo heat map, density heatmap

What is a geographic heatmap?

A geographic heatmap is a geographical representation of data that demonstrates where something occurs, specifying the areas of data’s high and low density. Geo heatmap does not limit displaying geospatial data to specified boundaries. Therefore, using the data’s location radius, it can cover a small and specific geographic area, as well as large regions, such as oceans or coasts. It uses color to highlight the areas of occurrence.

When should you use a geographic heatmap?

A geographic heatmap helps to understand the distribution and density of the given parameter on a map. The obvious application of geo heatmaps is weather forecasting, but there are many more spheres where it can be used: real estate, commerce, transportation, healthcare, environmental science, and many more.

When you shouldn’t use a geographic heatmap

Geographical heatmaps have the same core drawbacks as regular heatmaps. It gives an approximate view and can lack important details. Extremes can dominate, which can sometimes be misleading. You should also take into consideration that geo heatmaps heavily rely on colors. This means you should be really attentive to color palettes and take color blind viewers into consideration while designing it.

Choropleth map

What is a choropleth map?

A choropleth map is a type of map in which different administrative areas are colored (or shaded) according to the magnitude of their numeric value. The main difference between a choropleth map and a geographic heatmap is that a choropleth map uses border-defined areas, such as countries, states, or neighborhoods.

When should you use a choropleth map?

Choropleth maps display geographical data using normalized values, making them ideal for illustrating various rates and ratios across different regions.

When you shouldn’t use a choropleth map

Choropleth maps can be misleading when used to display raw data that is in some way connected to population. If a region has a high population, it might automatically show high absolute values for population-related indicators. That’s why it’s important to use per-capita values for choropleth maps.

Sometimes, the size of the geographical area can be misleading, either increasing or decreasing the perceived importance of the region. To minimize this influence, you could consider alternatives such as tile maps, where all regions are represented by icons of the same size.

Tile map

What is a tile map?

A tile map is a type of geographical map where a larger area (usually a country or a continent) is visualized by multiple equal-sized and shaped tiles, often square rectangles. Each tile represents a different region. A simple example of a tile map can be a collection of tiles forming the shape of the United States, where each tile corresponds to a state. What is important about tile maps is that all tiles do not vary in size, meaning that larger regions cannot dominate the visualization and smaller regions are not harder to read.

When should you use a tile map?

When the size of a geographical region is not relevant or might introduce a biased perception, a tile map is a perfect solution. Tile maps are also effective due to their clear structure and gridded view.

When you shouldn’t use a tile map

In cases where the shape of the geographical region is important, tile maps should be replaced by regular or choropleth maps.

Chord diagram

What is a chord diagram?

A chord diagram is used to show the structure of paired connections between the instances of the same level. Every instance is represented by an arc. Every connection is shown as a band with (potential) various start and end widths to depict differences in input and output.

When should you use a chord diagram?

Chord diagrams work well for showing the connection and the magnitude of the connection between the categories. It can reveal complex relationships that might be hard to find with other chart types. Common examples of chord diagrams vary from international trade flows to text and script analysis.

When you shouldn’t use a chord diagram

Chord diagrams might not work that well with large datasets and many connections. Many connections on the chord diagram might look busy and confusing.

Arc diagram

What is an arc diagram?

An arc diagram, in its essence, is similar to a chord diagram. While the chord diagram focuses mostly on the quantitative aspect of the connection, the arc diagram is more focused on the existence of the link. In an arc diagram, nodes are placed along a single baseline, and connections are drawn as arcs between them.. Arcs could be placed above and below the baseline, showing a different attribute of the connection.

Although the focus of the arc diagram is to show the existence of the connection, it can also be used to show the quantitative aspect of the connection using the thickness of the arc.

When should you use an arc diagram?

Arc diagrams are used for showing connections between pairs of categories. It allows us to see the structure of connections in a visual way.

When you shouldn’t use an arc diagram

Arc diagrams can be confusing to read if the dataset is too large. A lot of connection arcs can make the chart look cluttered and really hard to read.

Sankey

What is a sankey?

A Sankey diagram is a type of visualization that allows you to display flows from one set of values to another. It shows entities that represent the values and connects them by links, or flows. Each flow has a varying height, which depends on its quantity.

When should you use a sankey?

Sankey diagrams are commonly used to visualize supply chains, engineering and production processes, and energy efficiency. A known example is Google Analytics, which uses Sankey diagrams to depict the customer journey between pages of a website.

When you shouldn’t use a sankey

Sankey diagrams can be very difficult to interpret if the data is complex and the number of nodes and flows is extensive, making it hard to follow the flow. Additionally, they can fail when a precise comparison is needed. While visually appealing, Sankey diagrams can be challenging to interpret, and the audience will likely require an introduction to read them properly.

Network diagram

Alternative names: Network graph, Network mapping, Network visualization

What is a network diagram?

A network diagram is used to show the connections between multiple elements. The structure of the data and the purpose are somewhat similar to the arc diagram. But while in the arc diagram, all of the points are placed on the same line, in the network diagram positioning of the peaks can vary. In some variations of the network diagram, the position of the point depends on the number of connections this point has and the group it belongs to.

When should you use a network diagram?

Network diagrams are often used to show the clusters of members based on the intensity of the connections. Network diagrams are effective for showing patterns, clusters, and hierarchical relationships. All these types of connections are really hard to grasp all at once using any other chart.

When you shouldn’t use a network diagram

Network diagrams can also fail to be useful or meaningful in case the data lacks structure. They can easily become too crowded and complex if there are too many nodes and edges.

Flowchart

What is a flowchart?

A flowchart is a visualization of a workflow. It’s a diagram that depicts subsequent steps in the process. In other words, it shows what steps need to be followed to complete an action. A flowchart uses connecting lines and arrows to allow viewers to follow the process.

When should you use a flowchart?

A flowchart has many organizational use cases and can be a good tool to map out the customer journey and step-by-step instructions. It’s also popular in project management.

When you shouldn’t use a flowchart

A flowchart works best for simple structures. If you need to visualize a complex process or a large dataset, a flowchart is not the best choice.

That’s it for our 6 part series about chart types. Hope you enjoyed it. If you haven’t checked our previous parts you can find them here:

Part 1: Comparison charts
Part 2: Correlation charts
Part 3: Part-to-whole charts
Part 4: Data-over-time charts
Part 5: Distribution charts

Recently we’ve rewritten our essential article about 80 types of charts. Our main goal was to make the information structured and helpful right away. We talk about every chart starting from the look and feel and then going into practical advice on when you should and when you shouldn’t use this particular chart type. We are publishing parts of this article here. One chart group per each article.

In the fifth part, we’ll talk about distribution graphs. Distribution graphs are used to show how the data is spread. It is also can be used to show probability distribution. The essence of distribution graphs is to show how often each value appears in the dataset. The shape of distribution helps to gasp patterns which makes them a powerful tool for analysis in many fields. Histogram is the first option among distribution graphs.

Let’s explore distribution graphs one by one. Keep on reading!

Density plot

Alternative names: Kernel density plot, Density trace graph

What is a density plot?

A density plot is a type of chart that helps us visualize how the numeric data is being distributed over a period of time. Density plots somewhat resemble smooth peaks and valleys plotted between two axes. These correspond to a higher or lower concentration of values. A density plot is a variation of a histogram. However, it is visually more appealing, as it loses the sharp edges typical for histograms and adds a smooth continuous curve. The area below the line is usually transparent, which allows to show multiple distributions in one chart.

When should you use a density plot?

A density plot is a good choice for visualizing the distribution of continuous data. The specific case it is most useful is when you have multiple distribution curves and you want to compare them. The transparent nature of density plots allows one to see all the shapes of distribution curves as well as overlapping areas,

When you shouldn’t use a density plot

Avoid a density plot when you need every detail of the distribution. Density plots tend to smooth the details. Also, watch carefully for the readability. Too many distribution lines can make the chart cluttered.

Ridgeline plot

Alternative names: Joy plot, Joyplot

What is a ridgeline plot?

A ridgeline plot is a somewhat special type of chart. A ridgeline plot shows the distribution of a numeric value for several groups of a category. It is done by illustrating partially overlapping line plots (that can be made of density plots or histograms), which then can resemble a mountain range.

When should you use a ridgeline plot?

A ridgeline plot is used to visualize distribution over time (and space). It can be useful in case there are too many distribution lines for a density plot. The structure of the chart allows for the placement of a lot of distribution lines without losing readability. The limit is only the size of the chart.

When you shouldn’t use a ridgeline plot

The form of the ridgeline has some special features. Separate lines should have more or less the same form or have minor step-by-step changes. If the form of the lines is drastically different, some of the details might not be visible as they will be hidden by other charts. So it’s a trial-and-error process for this chart. You should try your data beforehand and see if everything important can be seen on the chart.

Horizon chart

What is a horizon chart?

A horizon chart is what you get when you compress a line or area chart into narrow stripes. To understand how it’s made, imagine you have an area chart with a range from -10 to +10 made of paper. You cut the chart say every 5 notches. After that, you place them above each other in ascending order. The result will be a horizon chart.

The difference with other charts is that both the positive and negative values are shown above the baseline, instead of showing negative values under the baseline. This allows you to show a lot of data in a very condensed manner.

Usually, to create a horizon chart, a diverging color scheme is used. This allows you to show both positive and negative values.

When should you use a horizon chart?

When you are dealing with a lot of categories and you want to make efficient use of space, this chart is the way to go. It is perfect to show time series data on the horizontal axis. Also, values on the vertical axis can be represented by colored bands. This makes it possible to show great precision of the values.

When you shouldn’t use a horizon chart

If you have enough space for a regular area or line chart and your audience is not familiar with the horizon chart, you definitely shouldn’t use the horizon chart. This chart is really hard to read intuitively; you really need to know the structure of how it’s made to use it effectively.

Histogram

Alternative names: Frequency distribution graph, Frequency distribution chart

What is a histogram?

A histogram is a type of chart that visually resembles a column chart. It’s a graph that consists of vertical rectangles (columns), whose length is proportional to the frequency of a variable (data items). The main visual difference between a histogram and a column chart is that there is no gap between the columns. That’s because, in a histogram, the values are grouped into ranges (bins). Each column’s height shows how many items fall within that range.

When should you use a histogram?

A histogram is a first option for visualizing a distribution. They are pretty versatile and can be used both to show an overall view when large bins are used and a detailed view if small bins are used. There’s also a specific formula to calculate the size of a bin for general use.

When you shouldn’t use a histogram

Most of the issues are connected to the visual similarities of the histogram and the bar chart. To recap it once again. Bar charts are used for comparing categories. Histograms are for showing distribution, which shows how many items fall into a certain data range.

Radial histogram

Alternative names: Angular histogram, Circular histogram, Polar histogram

What is a radial histogram?

A radial histogram is simply a variation of a histogram (see above), but with columns wrapped around a circle. It functions the same way as a regular histogram.

When should you use a radial histogram?

If a regular histogram might seem boring for your audience, you can try a radial histogram. It is not as accurate and easy to perceive as the regular histogram, but it’s very likely going to grab your readers’ attention.

When you shouldn’t use a radial histogram

When you need to deliver accurate data to a scientist or analyst, a radial histogram is not a good choice. The more important and serious the data is, the less likely a radial histogram is a good option.

Strip plot

Alternative names: Individual value plot, Single-axis scatter plot

What is a strip plot?

A strip plot is a variation of a scatter plot, but it only has one categorical and one numerical axis. It is a chart used to illustrate the distribution of many individual one-dimensional values. These values look like transparent dots located along a single (category) axis. If some of the dots have the same value, they can overlap, creating something that looks like a strip. Unlike a histogram and a density plot, the values are not placed in bins. Distribution is shown here via the density of overlapping dots.

When should you use a strip plot?

A strip plot works well for showing the distribution of several categories. It works best when the number of values is under 20–30 and there’s no intense overlapping of dots. A strip plot is also useful when you want to show the exact values. Another advantage of this plot is its space efficiency.

When you shouldn’t use a strip plot

When the number of values exceeds 30, the overlapping of dots might look overwhelming and not informative, even if they are transparent.

Jitter plot

Alternative names: Jittered strip plot, Jittered individual value plot

What is a jitter plot?

A jitter plot is an alternative to a strip plot (see above). It is used to visualize the distribution of several categorical variables. The main difference from a strip plot is that the dots used in the charts are shifted on the horizontal y-axis. This avoids overlapping (overplotting), which in turn avoids a lack of clarity.

When should you use a jitter plot?

Jitter plots have basically the same advantages as strip plots, but the number of values that can be shown under one category is more than for strip plots. This is due to additional space that is available via random shifts of dots.

When you shouldn’t use a jitter plot

In some cases, the randomness of the dots shift can confuse the reader. The downside of the jitter plot is that you need to manually control the level of the jitter so that all the dots are more or less visible. Another thing you need to control is the overlap of the dots that belong to neighbor categories; they should be distinguishable from each other.

One dimensional heatmap

What is a one-dimensional heatmap?

A one-dimensional heatmap is a chart that shows the distribution of a variable or category using colored stripes. Basically, you can get a one-dimensional heatmap if you take only one category from a heatmap. The visual difference is that in most cases, narrow and tall stripes are used instead of rectangles.

When should you use a one-dimensional heatmap?

If you want to zoom in on one category and focus on the evolution of that variable, you can use heatmaps in only one dimension. These charts are very popular in climate communication and often visualize temperatures.

When you shouldn’t use a one-dimensional heatmap

As one-dimensional heatmaps depend heavily on color, they can fail to deliver information to people with different types of color blindness.

Beeswarm chart

Alternative name: Swarm plot

What is a beeswarm chart?

In a beeswarm chart, dots are used to show the distribution of a variable. Values are each represented by one dot, and the swarm of dots represents the distribution found in the data. Instead of packing them in bins, the dots are scattered around each other and plotted on a single axis. This kind of chart is very useful when you want to display a lot of data points at once.

When should you use a beeswarm chart?

If you want to use dots to show the distribution of a variable and it’s important that dots won’t overlap, a beeswarm chart is your choice. The additional advantage of the beeswarm chart is that you can assign an additional variable to the size of the dot.

When you shouldn’t use a beeswarm chart

Beeswarm charts are constructed in a way that the dots are placed with some approximation. It means that the position of every dot might not be accurate. It means if the position of every dot is important to you, you should choose another chart: a histogram, a density plot or a strip plot.

Box chart

Alternative names: Box plot, Boxplot, Box-and-whisker plot/chart, Whisker plot

What is a box plot?

A box chart is used to depict the distributions of one or more groups of numeric data. It consists of several elements. The box showing the middle 50% оf the data is also called the interquartile range (IQR). The line inside the box shows the median of the data. The rest of the data is usually shown using whiskers on the left and right sides of the box (in case of a horizontal box plot). Sometimes there are outliers in the data. In this case, the whisker is limited to 1.5 of the interquartile range, and the rest of the values are shown using dots.

When should you use a box plot?

Box plots are meant to provide a high level of information at a glance — a summary of data. This type of graph is quite popular in the research and financial fields. So a box plot is a perfect solution for a quick overview of the data when your audience is familiar with statistical charts.

When you shouldn’t use a box plot

A box plot can be quite confusing for the general public. If your readers aren’t familiar with statistical charts, it’s better to use something simpler, like a histogram or range plot. Also, if you want to show detailed information, a box plot is not the best choice, as it works better for an overview of the data.

Violin plot

What is a violin plot?

A violin plot is a hybrid of a box plot and a density plot. Thanks to this, a violin plot depicts distributions of numeric data for one or more groups using density curves. Visually, it resembles a violin, hence its name.

When should you use a violin plot?

A violin plot combines the best aspects of a box plot and a density plot. This versatility allows it to be used for various audiences, from a general audience to scientists and analysts.

When you shouldn’t use a violin plot

For a small dataset, a violin plot can be an excessive option. It can also fail for visualizing outliers. In this case, a box plot would be a better option.

That’s it for distribution charts. In the next part we’ll explore geospatial maps and other unusual types of charts. Take care and see you soon!

You can also check all other parts here:
Part 1: Comparison charts
Part 2: Correlation charts
Part 3: Part-to-whole charts
Part 4: Data-over-time charts
Part 6: Geospatial and other charts

Date-over-time graphs are used to visualize trends and changes in data over a specific period. The X axis usually represents time, the Y axis the value shows the tracked data. Most common and useful time series chart type is line chart.

Area chart

What is an area chart?

An area chart builds on a line chart. You plot data points, connect them with a line, and then shade the space between the line and the x‑axis. The filled area shows how a total grows or shrinks over time, so the reader sees volume as well as trend.

When should you use an area chart?

Choose an area chart when you want to show the magnitude of a measure, not just its trend. The filled space draws the eye to how much has accumulated or changed over time. This is helpful for totals that start at zero, such as monthly revenue, website visits, or rainfall. Pick an area chart when the story is about “how much” in addition to “going up or down.

When you shouldn’t use an area chart

Avoid an area chart when you need to compare precise values or show small fluctuations. In this case, a simple bar chart is better to use. Moreover, the filled shape can hide fine detail, and if the data does not start at zero, the shaded area can mislead. In this case, a line chart is clearer.

Stacked area chart

Alternative name: Stacked area graph

What is a stacked area chart?

A stacked area chart shows how several groups change over time. Each group’s area is placed on top of the previous one, so the chart displays both the total and the contribution of each group at every point.

When should you use a stacked area chart?

Use a stacked area chart to show the overall trend of a total and how several groups contribute to that total over time. It works well for data that starts at zero and where the order of the series does not distract from the main message. This works, for example, for monthly revenue broken down by product line or daily energy use split by source. The filled layers let readers see growth or decline in the whole while still spotting which groups drive those changes.

When you shouldn’t use a stacked area chart

Skip a stacked area chart if the main goal is to compare the subgroups with each other rather than the combined total. Upper layers sit on shifting baselines, which makes their exact sizes hard to judge. The chart also becomes messy with many small categories, as colours blend and labels overlap. In those situations, a grouped bar chart or multiple line charts will communicate the data more clearly. Another alternative is a percent‑stacked area chart. This chart rescales each time step to 100 percent so the layers show how each group’s share of the whole changes over time.

Stream graph

Alternative names: Streamgraph, ThemeRiver

What is a stream graph?

A stream graph is undoubtedly one of the most beautiful chart types available. This stunning type of chart is a variation of a stacked area chart. Instead of piling layers on a fixed x‑axis, it balances them around a central baseline. The layers rise and fall like a flowing river, so the chart looks smooth and organic. Peaks and dips show how each series grows or shrinks over time, and the overall shape can hint at seasonal patterns.

When should you use a stream graph?

Choose a stream graph when you want to highlight broad trends in multivariate time‑series data and add visual appeal. It works well for social‑media analysis over months, web‑traffic sources through a year, stock‑market sectors, or climate indicators. The floating baseline reduces the eye’s focus on exact numbers and lets viewers see how the mix of categories shifts over time.

When you shouldn’t use a stream graph

Avoid a stream graph if precise comparisons matter. Because layers rest on a moving baseline, it is hard to read exact values or small differences. The chart also becomes confusing when you have many thin layers or highly volatile data. In those cases, a standard stacked area chart, a grouped line chart, or small multiples will be clearer.

Bump chart

What is a bump chart?

A bump chart is a line chart that shows rankings over time. The x‑axis lists time points, the y‑axis lists rank positions (1 at the top, larger ranks below). Each category is drawn as a line that rises or falls as its rank changes. Small data marks can be placed at each time point to emphasise the “bumps” where positions shift.

When should you use a bump chart?

Use a bump chart when you care about who is ahead, who slips, and how often places change. It fits sports standings, sales leaderboards, election polls, or any series where order matters more than exact numbers. Limit the chart to a handful of lines so viewers can follow the crossings.

When you shouldn’t use a bump chart

Avoid a bump chart if you need the actual values behind the ranks, because every step on the y‑axis is equal, even when the underlying differences are not. The chart also gets cluttered with many categories or frequent ties. For precise comparisons or large datasets, a regular line chart or a table is clearer.

Bump area chart

What is a bump area chart?

A bump area chart combines ideas from a bump chart and a stream graph. Each band shows the value of a category over time (area height) while its vertical position shows the rank among all categories at each moment. The bands are sorted by rank, so you see who is on top, who moves up or down, and how big each category is at the same time.

When should you use a bump area chart?

Choose a bump area chart when you need to show both how large each category is and how their rankings change. It works well for data like market share by company, streaming minutes by platform, or vote counts by party, where magnitude and position both matter.

When you shouldn’t use a bump area chart

Skip this chart if the audience cares only about rank; a plain bump chart is simpler. It can also overwhelm readers who are not used to this type of chart, or when many categories crowd the view. In those cases, use a line chart, bump chart, or small multiples instead.

Line chart

Alternative names: Line graph, Line plot

What is a line chart?

A line chart plots data points on an x–y grid and connects them with straight lines to show how a value changes over a continuous variable, most often time. The x‑axis holds the time scale (or any ordered measure), while the y‑axis shows the numerical metric you want to track. One chart can display a single series or several lines for comparison.

When should you use a line chart?

Use a line chart when your x‑axis is a continuous scale such as time and you need to show overall trends, growth, or decline. Because the scale does not have to start at zero, you can zoom in on small movements and spot patterns that a bar chart might hide. Line charts are versatile and quick to read, making them essential in finance, science, and business dashboards. They also combine easily with other visuals. Examples are overlaying bars for totals or shading under the line to form an area chart.

When you shouldn’t use a line chart

Reserve line charts for closely spaced, continuous data where trends matter more than exact values. When dates are far apart, the connecting line can imply change that never happened, so use bars or dots instead. If precision is key, switch to a bar chart or dot plot where each value is easy to read.

Moreover, a tangle of many lines soon overwhelms. Best practice is to highlight the main series and fade the rest. Finally, skip dual‑axis line charts. Two different scales on one graph mislead. It is therefore better to use two simple line charts side by side instead.

If you’d like to design really stunning line charts, make sure to see our line chart resource page full of great tips and more line chart examples.

Spline chart

Alternative names: Spline graph, Curve chart

What is a spline chart?

A spline chart works like a line chart but replaces straight line segments with a smooth curve. This curving line gives the trend a softer, more continuous look. Combine the curve with a filled area and you get a spline area chart.

When should you use a spline chart?

Choose a spline chart when your data change smoothly over time, and you want to reflect that gentle flow in the visual. Examples are average temperatures or rolling sales figures. The curved line is also more visually appealing than sharp angles. So it works well in presentations or dashboards where design matters as much as the message.

When you shouldn’t use a spline chart

Avoid a spline chart when precise inflection points matter, because the curve can hide sharp jumps. If data moves in abrupt steps or you need readers to see each exact point, use a standard line chart instead. Also, skip the spline if you only have a few data points. The smoothing can create misleading dips or peaks between them.

Step line chart

Alternative names: Step chart, Stepped line graph

What is a step line chart?

The step line chart only uses horizontal and vertical lines to connect the data points. It is convenient to use when you want to highlight the exact moment in time when the data changes.

When should you use a step line chart?

Use it when your data shifts at irregular moments and you want to show exactly when each change happens. Typical cases are price updates or inventory restocks.

When you shouldn’t use a step line chart

Skip a step line chart if the data changes at regular intervals or almost every time point. In that case, a standard line chart will show the pattern with less visual clutter.

Candlestick chart

Alternative name: Japanese candlestick chart

What is a candlestick chart?

A candlestick chart plots price action over time. Each “candle” has a thick body that marks the opening and closing prices and thin wicks that show the highest and lowest prices for that period. Bodies can be colour‑coded to see instantly which periods fell and which rose. Typically, they are colored red when the price closes lower than it opened and green when it closes higher.

When should you use a candlestick chart?

Use a candlestick chart for financial data when you need to see intraday or period‑by‑period price moves and spot patterns that hint at future trends. Traders and analysts rely on it for stocks, forex, and cryptocurrency because it shows volatility and market sentiment at a glance.

When you shouldn’t use a candlestick chart

Avoid a candlestick chart if you only need a single closing price per period or if your audience is not familiar with trading visuals. The extra detail can distract beginners. For basic end‑of‑day prices, a simple line or bar chart is clearer.

If you’re interested in designing a candlestick chart and adding it to your financial report, it’s possible to create it with Datylon for Illustrator.

You can read more about creating a candlestick chart in our article.

Gantt chart

What is a Gantt chart?

A Gantt chart is a bar chart that maps tasks against time. The y‑axis lists activities or tasks, and each task is drawn as a horizontal bar whose left end marks the start date and whose right end marks the finish date. The x‑axis shows the calendar, so you can see instantly when each task begins, how long it lasts, and where tasks overlap.

When should you use a Gannt chart?

Use a Gantt chart to plan or track work that unfolds over time. It is a staple in project management for monitoring schedules, deadlines, and progress, but the format suits any data that describes time blocks. You can plot crop seasons, service availability, or animal migration periods with the same layout. Any situation where “what happens when” is the key question is a good fit.

When you shouldn’t use a Gannt chart

Skip a Gantt chart if the horizontal axis is not time. When the bars represent values instead of dates, use a range bar chart instead. Also, avoid a Gantt if you need to compare the exact duration of tasks; a simple bar chart that shows length alone will make those comparisons clearer.

Barcode chart

What is a barcode chart?

A barcode chart lays very thin bars along a single axis to show the distribution of many observations. Each bar marks one data point. You can keep all bars the same thickness or vary the thickness and colour to encode extra information. The result looks like the stripes of a product barcode.

When should you use a barcode chart?

Use a barcode chart when you have many observations on one long scale but little space to show them. It works well as a compact alternative to a strip plot when the data marks would otherwise overlap. You can thicken some bars or colour them differently to highlight special states, such as events of interest or different categories.

When you shouldn’t use a barcode chart

Avoid a barcode chart if readers must see exact values or compare precise differences. The thin bars give only position, not magnitude. It is also not suitable when you need many colour classes, because thin stripes make the colours hard to distinguish.

OHLC chart

What is an OHLC chart?

An Open-High-Low-Close (OHLC) chart plots price action along a time axis. For each period, it draws a vertical line from the low to the high price, then adds two short horizontal ticks. The tick on the left marks the opening price, and the tick on the right marks the closing price. Often, the symbol is coloured green when the close is above the open and red when it is below. This way, you can see rising and falling periods instantly.

When should you use an OHLC chart?

Use an OHLC (Open‑High‑Low‑Close) chart when you need a compact view of all four key prices in each trading interval. It is a staple in finance for analysing stocks, forex, and other markets because it shows volatility and direction without the filled bodies of candlesticks, making dense timelines easier to read.

When you shouldn’t use an OHLC chart

Skip an OHLC chart if your audience only needs closing prices or if they are unfamiliar with trading visuals. In those cases, a simple line chart or bar chart of closing prices will be clearer. If the focus is on pattern recognition and you want the visual weight of filled candles, a candlestick chart may be a better choice.

That’s it for data over time charts. In the next part we’ll explore distribution charts. Take care and see you soon!

You can also check all other parts here:
Part 1: Comparison charts
Part 2: Correlation charts
Part 3: Part-to-whole charts
Part 5: Distribution charts
Part 6: Geospatial and other charts

Recently we’ve rewritten our essential article about 80 types of charts. Our main goal was to make the information structured and helpful right away. We talk about every chart starting from the look and feel and then going into practical advice on when you should and when you shouldn’t use this particular chart type. We will start publishing parts of this article here. One chart group per each article.

In the third part, we’ll talk about part-to-whole graphs. Part-to-whole graphs are used to represent how individual pieces contribute to a complete picture. They focus on composition and proportions. Stacked bar chart in majority of the cases tend to be the best option amount part-to-whole graphs.

Let’s explore part-to-whole graphs one by one. Keep on reading!

Stacked bar chart & stacked column chart

What is a stacked bar chart?

A stacked bar chart builds on a regular bar (or column) chart by slicing each bar into coloured segments. The full bar represents the total for a category, and each segment shows how much a sub‑category contributes to that total. If you scale every bar to the same length, you create a 100 percent stacked version. In this case, the segments display relative shares rather than absolute values.

When should you use a stacked bar chart?

Use a stacked bar chart to show the overall size of each category and how its sub‑categories contribute to that total. It works best when the number of segments is small and their order is consistent. This way, the reader can quickly see which category is largest, what makes it up, and how those parts compare across categories. A 100 percent stacked version is handy when you care more about proportional shares than absolute values.

When you shouldn’t use a stacked bar chart

Avoid stacked bars if there are many segments. Thin stacks are hard to label and even harder to compare. They are also a poor choice when the goal is to compare individual segment sizes across categories. This is because only the bottom segments share a common baseline. If precise segment‑to‑segment comparison matters, a grouped bar chart or small multiples will be clearer.

Stacked bars also struggle with negative values or datasets where segments appear in some categories but not others. Both make the visual harder to read.

Diverging (stacked) bar/column chart

What is a diverging bar chart?

A diverging bar chart is a variation of a regular bar chart. Unlike a standard bar chart, it has a baseline in the center, usually set at zero. Bars extend in both directions from this midpoint. This makes it easy to compare values that move in opposite directions. For example, bars on the left often show negative values or sentiment, while bars on the right show positive values or sentiment.

The design typically relies on a diverging color palette. It consists of one hue for the bars that extend left and a contrasting hue for those that extend right. This way, readers can instantly see which side of zero each value falls on.

A common variation is the diverging stacked bar chart. In this chart, each side of the baseline is split into colored segments to show how different subgroups contribute to the negative or positive totals.

When should you use a diverging bar chart?

Choose a diverging bar chart when your measure can move in two directions and you want to compare those directions side by side. It is ideal for data that mixes positive and negative values. Examples are profit versus loss, approval versus disapproval, or temperature above and below average. Survey results on Likert scales are a common example, but any dataset with a natural midpoint (zero or neutral) can benefit from this format.

When you shouldn’t use a diverging bar chart

Do not pick a diverging bar chart unless your data truly has a meaningful midpoint. The centred baseline implies a clear split into two opposite directions, often negative on the left and positive on the right. If your values are all positive, all negative, or lack a natural “zero,” this chart will mislead.

Also, limit the number of stacks. When there are many thin segments, it becomes hard to see or label them, and even harder to compare matching stacks across categories. Finally, if readers need to measure exact differences between individual segments, a grouped bar chart or small multiples will be easier to read.

Population pyramid

Alternative names: Age-sex pyramid, Age structure diagram

What is a population pyramid?

A population pyramid looks like a diverging bar chart. However, it is used only to show how a population is distributed by age and sex. Bars for one sex extend to the left of a central baseline, and bars for the other sex extend to the right. Each horizontal layer represents an age group. So the overall shape reveals whether a population is young, ageing, or balanced.

When should you use a population pyramid?

Choose a population pyramid when your data list age together with exactly two gender categories. It gives a quick snapshot of how each age band splits between the two and is standard in demographic and population studies to spot youth bulges, ageing trends, or gender imbalances.

When you shouldn’t use a population pyramid

Skip a population pyramid if your data is not limited to two sex categories. The classic left‑versus‑right layout forces everything into a male‑female split and hides people who identify outside that binary. In this case, use a chart that can show three or more groups. Examples are side‑by‑side bars or small multiples.

A population pyramid is also the wrong chart type when age bands are uneven or for comparing many regions in one frame. A grouped bar chart or small multiples will be clearer in those cases.

Icon array

Alternative name: Pictograph

What is an icon array?

An icon array is a grid of small icons arranged in rows and columns. The entire grid represents the total number of units in the dataset. Each icon stands for one unit, such as a person, a vote, or an item. Icons that belong to the same subgroup share a colour or shape, letting you see quickly how the whole divides into its parts. A ten‑by‑ten grid is common because it neatly shows one hundred units, but other grid sizes can be used just as well.

When should you use an icon array?

Use an icon array when you need a quick part‑to‑whole picture that anyone can understand instantly. Each icon equals one unit, so readers can count symbols instead of decoding percentages or tables. The format suits survey results, market shares, or demographic splits. It also works for simple yes‑or‑no data where filled icons show “yes” and the remainder show “no.” Originating with the Isotype movement, icon arrays remain a clear way to turn numbers into pictures.

When you shouldn’t use an icon array

Avoid icon arrays when counts are very large or uneven, for example 13 487. A grid that big is cluttered, and rounding can mislead. This type of chart also struggles with small differences. The smallest change you can make, adding or removing a single icon, often represents a sizable chunk of the data. Moreover, with more than a few categories, the chart becomes cluttered. If there are more than 5 categories, we recommend adding a “rest” category.

Visit our icon array resource page for pro tips on designing clear and effective icon arrays.

Waffle chart

Alternative names: Square pie chart, Square area chart, Gridplot

What is a waffle chart?

A waffle chart looks like an icon array but replaces icons with a grid of 100 squares cells. Each cell equals one percent of the total. By colouring groups of cells, you can show how much of the whole is complete, how far you are from a goal, or how several categories contribute to one hundred percent.

When should you use a waffle chart?

Choose a waffle chart when you need a quick, part‑to‑whole picture that even non‑technical readers can understand. It works well for progress indicators and survey results, and any other data where you want to turn percentages into a neat, countable grid. Because its layout is square, some audiences find it easier to read than a pie chart.

When you shouldn’t use a waffle chart

Skip a waffle chart if your numbers are not close to whole percentages, since you must round to the nearest cell, and that can distort small values. It also becomes cluttered when you have more than three or four categories. In this case, the coloured blocks start to blend, and labels get hard to place. For precise comparison between multiple groups, a bar or stacked bar chart will be clearer.

Pie chart

Alternative names: Pie graph, Pizza chart, Circle chart

What is a pie chart?

A pie chart is a circular graphic for showing how a total breaks into parts. The full circle represents the whole, while each slice represents a category’s share of that whole. The slices must add up to a meaningful total, which is often 100 percent. If your data are absolute numbers such as dollars or units, all categories together should equal a sensible total.

When should you use a pie chart?

Use a pie chart when you have only a few categories and you want readers to see their relative sizes instantly. It is especially effective when one slice is much larger or smaller than the others, because the difference stands out immediately.

When you shouldn’t use a pie chart

Avoid a pie chart when you need precise comparisons. Human vision judges angles and curved areas poorly, so small differences between slices are hard to read. The chart is also limited if you have many categories, because narrow wedges become crowded and labels overlap. For detailed or multi‑category comparisons, a bar chart, stacked bar, or another linear chart will communicate the data more clearly.

Read the deep dive pie chart article to see our arguments for using pie charts. And if you want to create a really good pie chart yourself, don’t miss out on the pie chart resource page full of pro design tips.

Donut chart

Alternative names: Doughnut chart

What is a donut chart?

A donut chart is a pie chart with a circular hole in the centre. The ring still shows how a total splits into slices, but the empty middle frees up space for extra information.

When should you use a donut chart?

Choose a donut chart when you want the simple part‑to‑whole message of a pie chart plus room in the centre for a title, key metric, or icon. The ring shape also shifts the read from comparing wedge areas to comparing arc lengths, which many viewers find a little easier. Use it for small sets of categories where one or two slices dominate and a quick visual impression is enough.

When you shouldn’t use a donut chart

Skip a donut chart if you need precise comparisons or have many categories. Thin wedges are hard to judge and to label, so a stacked bar chart or grouped bar chart will show detailed differences more clearly.

For your convenience, we also created a donut chart resource page with valuable design tips for your next donut chart.

Semicircle donut chart

Alternative name: Half moon chart, Half donut chart, Semi-circle doughnut chart

What is a semicircle donut chart?

A semicircle donut chart is a donut chart sliced in half. All the pieces still add up to 100 percent, but they are arranged over only half of the circle. This forms a half‑moon shape with a straight edge at the bottom.

When should you use a semicircle donut chart?

Use a semicircle donut when you want the part‑to‑whole story of a donut chart, yet need extra blank space for labels, numbers, or a pointer. The flat edge also makes it a handy base for simple gauge visuals that show progress toward a target.

When you shouldn’t use a semicircle donut chart

Avoid this chart for precise comparisons or when you have many categories. Narrow wedges become hard to read, and judging arc lengths in a half‑circle is even tougher than in a full circle. In those cases, a stacked or grouped bar chart will communicate the data more clearly.

Explore more pie and donut chart ideas on our inspiration page.

Marimekko chart

Alternative names: Mekko chart, Mosaic chart, Mosaic plot

What is a Marimekko chart?

Think of a Marimekko chart as stacked bars that can vary in width. The width of each bar shows one measure. Each bar is then divided into coloured stacks whose heights show a second measure. All the bars fit together without gaps, so you can see both how big each bar is compared with the total and how each bar’s pieces break down inside it.

When should you use a Marimekko chart?

Use a Marimekko chart to compare part‑to‑whole relationships across two dimensions at once. It is popular in marketing and sales analysis. It can show, for example, how revenue is split by region (column width) and by product mix inside each region (segment height). Any dataset that needs to display two nested proportions in a single view can benefit from this format.

When you shouldn’t use a Marimekko chart

Skip a Marimekko chart if there are many columns or many small segments. Narrow columns and thin slices are hard to label and even harder to compare. The chart is not ideal for precise reading. So if you need exact comparisons, a grouped bar chart, a stacked bar chart, or a pair of simpler bar charts will be clearer.

Treemap

What is a treemap?

A treemap is a rectangular diagram that shows how a total breaks down into parts. Each top‑level category gets its own rectangle, and you can split these into smaller rectangles for sub‑categories. The area of every rectangle is sized in proportion to the value it represents. This lets you instantly see which parts are large and which are small.

When should you use a treemap?

Choose a treemap when you have many categories arranged in a hierarchy and need to display all of them in one compact view. The chart makes good use of space, so it can show a large dataset on a single screen while still revealing relationships within and between groups. This makes treemaps popular for file‑system usage, market share by brand and product line, budget breakdowns, and similar tasks.

When you shouldn’t use a treemap

Avoid a treemap if it will contain too many tiny rectangles. An overfilled grid becomes hard to read and overwhelms the viewer. Treemaps also struggle with precise comparison because people judge rectangular areas less accurately than bar lengths. When exact values or fine differences matter, a bar chart, stacked bar chart, or separate small multiples will communicate the data more clearly.

For more background, a deep‑dive article and a detailed treemap resource page are available if you would like extra tips before building your own.

Circular treemap

Alternative name: Circular packing, Circle packing

What is a circular treemap?

A circular treemap uses circles instead of rectangles to show a hierarchy. Each top‑level category is drawn as a large circle, and these can be divided into smaller circles for sub‑categories. The area of every circle matches the value it represents, so larger values appear as larger circles within the overall packing.

When should you use a circular treemap?

Choose a circular treemap when you want to display a hierarchy in a way that feels organic and visually engaging. Although it is less space‑efficient than a rectangular treemap, a well‑designed circle packing can be eye‑catching and easy for viewers to explore. It works best for overviews where the exact proportions are less critical and visual appeal is a priority.

When you shouldn’t use a circular treemap

Avoid a circular treemap when precise comparison matters more than showing the hierarchy. People judge circle areas less accurately than rectangle sizes or bar lengths, so fine differences between values can be hard to spot. If space is tight or you need exact comparisons, a rectangular treemap or a bar chart will serve better.

Convex treemap

Alternative names: Voronoi treemap, Polygonal partition

What is a convex treemap?

A convex treemap visualises hierarchical data in the same way a regular treemap does. However, it replaces rectangles with convex polygons. These are shapes with no inward dents, such as hexagons or irregular blobs whose edges always bulge outward. A simple way to tell if a polygon is convex is the following. Draw a straight line between any two points inside the shape, and that line will stay entirely inside the shape.

Because these polygons can be warped and packed into almost any outline, the entire chart can fit inside shapes such as circles, triangles, or custom silhouettes. Each polygon’s area still represents the value of a category, and polygons can be further subdivided to show sub‑categories. So the hierarchy remains intact while the overall layout looks more organic and space‑efficient than a strict rectangular grid.

When should you use a convex treemap?

Use a convex treemap when the chart must fit a non‑rectangular shape or avoid skinny tiles in deep hierarchies. Its flexible polygons suit infographics with custom outlines, dashboards with tight spaces, and visuals that benefit from an organic look.

When you shouldn’t use a convex treemap

Avoid a convex treemap when exact area comparison matters. Irregular polygons are harder to judge than rectangles, and the view turns cluttered when many small categories appear. This layout also takes more computation to generate. If a rectangular grid will do, a standard treemap or bar chart is clearer.

For an example of a well‑designed convex treemap, see our treemap deep‑dive article.

Dendrogram

Alternative name: Phylogenetic tree

What is a dendrogram?

A dendrogram is a tree‑shaped diagram that shows how items cluster into a hierarchy. It begins with each item as an individual leaf; branches then merge step by step until all items join a single trunk. The height where two branches join represents the distance between those items. Clusters near the bottom reflect the raw data most directly and are the most reliable. As you move up the tree the merges rely on earlier averages, so the relationships become progressively less precise.

When should you use a dendrogram?

Use a dendrogram when you need to present the results of hierarchical clustering. Biologists rely on it to map evolutionary links among species, while other fields use the same tree layout for gene clustering, customer segmentation, or grouping similar documents. The branching structure lets readers see which clusters lie close together and where the major splits occur.

When you shouldn’t use a dendrogram

Avoid a dendrogram if the hierarchy is shallow or the dataset is huge. Too few levels give a tree that could be shown more clearly as simple groups. On the contrary, hundreds of leaves produce a tangle of lines that is hard to read. If your main goal is to compare cluster sizes or values, use a bar chart or heatmap instead.

Venn diagram

Alternative name: Set diagram, Logic diagram

What is a Venn diagram?

A Venn diagram uses overlapping circles to show every possible intersection among two or more sets, even when some intersections contain no items. Shared regions reveal the elements that the sets have in common. Non‑overlapping regions hold elements unique to each set. Because the layout includes empty overlaps as well as real ones, it presents the full logical picture of how the sets could relate.

When should you use a Venn diagram?

Choose a Venn diagram when you want a familiar, easy‑to‑read picture of how two or three sets relate, including places where they could overlap but do not. This makes it useful in lessons, presentations, and simple reports where highlighting both real and potential intersections helps explain the concept.

When you shouldn’t use a Venn diagram

Skip a Venn diagram if you have more than three sets or if you care only about the overlaps that actually exist. Extra circles or empty intersections quickly make the graphic cluttered. In those situations, an Euler diagram, which shows only real overlap, will be clearer.

Euler diagram

What is an Euler diagram?

An Euler diagram (pronounced “OY‑ler”) looks a lot like a Venn diagram, but it shows only the overlaps that actually exist in the data. In a Venn diagra, every possible intersection appears, even if no items fall in those intersections. An Euler diagram omits any empty intersections. So each region of the drawing corresponds to a real‑world set or overlap and nothing more.

When should you use an Euler diagram?

Use an Euler diagram to show only the overlaps that truly exist. It works well for two or three sets that share some items yet stay distinct elsewhere. This lets readers focus on real connections without empty intersections.

When you shouldn’t use an Euler diagram

Do not use an Euler diagram when you must display every possible intersection, even ones that are empty. It is also limited when you have many sets or very complex overlaps, as the visual gets messy. In those situations, choose a matrix chart instead.

If you’re curious to understand it better, we recommend this article that explains the difference between the Venn and Euler diagram.

Circular gauge

Alternative names: Angular gauge, Radial gauge chart

What is a circular gauge?

A circular gauge shows a single value on a round or half‑round scale. A needle (or pointer) sweeps across the scale to mark the current reading, much like the dial on a speedometer or an analogue clock. Coloured bands on the arc can signal safe, warning, or critical zones.

When should you use a circular gauge?

Use a circular gauge when you need a quick, instant check of one key metric in a dashboard. It works well for KPIs such as system load, sales progress, or temperature. Here, the viewer only needs to judge whether the value sits in a good, caution, or danger range.

When you shouldn’t use a circular gauge

Skip a circular gauge when you need to display several metrics side by side or when readers must read exact values. Multiple gauges quickly fill a dashboard and make comparisons awkward. Moreover, curved scales are harder to read precisely. In these situations, a bullet chart is a better choice. This chart shows exact numbers and lets you compare several values against reference ranges in a compact space.

Sunburst chart

Alternative names: Multi-level pie chart, Multilayer pie chart, Sunburst graph, Ring chart, Radial treemap

What is a sunburst chart?

A sunburst chart has many names, but whatever you call it, it’s a spectacular type of graph. It shows a hierarchical dataset through a series of concentric outward rings. Each of those rings corresponds to a different hierarchy level. The inner circle looks like a donut chart, but each outer ring can be sliced up depending on its relationship to the inner (parent) circle.

When should you use a sunburst chart?

Sunburst charts are often a good alternative to treemaps, but if you do opt for this type of chart, keep in mind that its radial layout takes more space than a rectangular shape of a treemap.

When you shouldn’t use a sunburst chart

Avoid a sunburst chart for very deep hierarchies or when there are many branches. Outer slices become thin, labels are hard to place, and comparisons across branches are difficult. If space is limited or you need easier comparisons, a treemap will work better.

Funnel chart

Alternative name: Triangle chart

What is a funnel chart?

A funnel chart stacks several horizontal bars on top of one another, widest at the base and narrowing toward the tip. Each band represents a level in a hierarchy, such as awareness, interest, and purchase in a marketing funnel.

When should you use a funnel chart?

Use these charts when you need a quick visual of how numbers shrink or grow as they move through ordered stages. They are common in sales and marketing dashboards to show how many prospects reach each step and what revenue is tied to those steps. They also work anywhere a clear top‑to‑bottom (or entry‑to‑exit) flow helps tell the story. They are widely used in infographics and business presentations and dashboards.

When you shouldn’t use a funnel chart

Avoid these charts when you need precise comparisons between stages. Because the bars taper, they do not share a common baseline, and people find it hard to judge their exact sizes. A standard bar or column chart shows differences more clearly. Pyramid and funnel charts also become unreadable if you include too many stages because narrow slices are hard to read and label.

That’s it for part-to-whole and hierarchical charts. In the next part we’ll explore data-over-time charts. Take care and see you soon!

You can also check all other parts here:
Part 1: Comparison charts
Part 2: Correlation charts
Part 4: Data-over-time charts
Part 5: Distribution charts
Part 6: Geospatial and other charts

Recently we’ve rewritten our essential article about 80 types of charts. Our main goal was to make the information structured and helpful right away. We talk about every chart starting from the look and feel and then going into practical advice on when you should and when you shouldn’t use this particular chart type. We will start publishing parts of this article here. One chart group per each article.

In the second part, we’ll talk about correlation graphs. Correlation graphs are used to explore relationships between two sets of data. They allow to see if changes in one variable are linked to changes in another variable. Correlation graphs are great to identify potential connections in the data. It’s also important to remember that correlation graphs show trends but not causation. The go to option for visualizing correlation are scatter plot and bubble chart.

Let’s explore correlation graphs one by one. Keep on reading!

Heatmap

Alternative names: Heat map, Heat table, Density table

What is a heatmap?

A heatmap shows values as colors in a grid or as a colored layer on a map. The palette makes patterns, trends, correlations, and density differences instantly visible. Adding a date or time scale to the x-axis lets you track how values change over time, helping you spot anomalies. In a matrix layout, it reveals correlations. On a geographic base, it highlights where a value is more or less concentrated.

When should you use a heatmap?

Heatmaps are most helpful when you need an initial view of the data and want to let the colors guide you. Using color is a very natural way of spotting patterns, trends, and correlations. So a heatmap quickly tells you where to look next: for outliers, other viewpoints, or specific angles. Typical situations include:

Exploring wide‑ranging data to see overall structure before deeper analysis
Monitoring changes over time by adding a temporal x‑axis
Showing spatial density on a geographic base layer
Revealing correlations in a matrix of two categorical variables
Focusing on the evolution of a single variable with a one‑dimensional heatmap (e.g., temperature variations)

When you shouldn’t use a heatmap

A heatmap is primarily an exploratory technique and less suited for precise comparison. If your audience needs to measure exactly how much one value differs from another, color alone can be ambiguous. Subtle differences may fade, and extreme values can dominate the palette. In such cases, choose a chart that expresses magnitude more explicitly with position or length. For example, with parallel coordinates, a multi‑series line or bump chart, or a bubble chart.

Check our heatmap resource page and discover pro tips on how to design the best heatmap chart yourself.

Bubble chart

Alternative name: Bubble plot

What is a bubble chart?

Deriving from a scatter plot, a bubble chart is a chart that looks at the relationship between three or more (numeric) variables. Two of those variables are represented by the y-axis and x-axis. The third value is represented by the size of a bubble.

With some expansions, a bubble chart can represent up to seven variables at once. You can add variables to color, stroke, icon, or label, for example. However, keeping the total to three or four variables keeps the chart readable. Analysts value bubble charts for spotting correlations, clusters, and outliers, and for their strong data‑to‑space ratio.

When should you use a bubble chart?

Use a bubble chart to explore relationships among several numeric variables, highlight clusters or extreme points, and keep the visual compact. Its ability to show multiple dimensions makes it popular for research dashboards and infographics. It also works in any setting where space is limited, but a richer view than a simple scatter plot is required.

When you shouldn’t use a bubble chart

Bubble charts lose effectiveness if you overload them with too many bound variables, or if the range of the size variable is so narrow that bubbles look alike. Dense overlaps can also hide information unless you tweak the opacity. In these cases, consider a simpler alternative such as a scatter plot, heatmap, or dot plot that focuses on fewer dimensions.

Refer to our bubble chart resource page and discover pro tips on how to design the best bubble chart yourself.

Scatter plot

Alternative names: Scatterplot, Scatter chart, Scattergram, Scatter diagram, Scatter graph

What is a scatter plot?

A scatter plot shows two numerical variables by placing dots at their X and Y coordinates. With every mark the same size, you get a clear view of how each observation sits on the two‑variable plane.

When should you use a scatter plot?

Choose a scatter plot when you need a quick look at possible correlations, patterns, clusters, or outliers. Its strong data‑to‑space ratio and simple structure make it the usual first step in data exploration. Because the layout is so flexible, a scatter plot can evolve into bubble charts, categorical scatter plots, quadrant charts, hexagon binning, or other hybrids.

When you shouldn’t use a scatter plot

Skip a scatter plot if dense overlaps make either the dots or their data labels hard to read. The same applies when readers need precise value‑by‑value comparisons rather than overall trends. They are also not ideal when variables are categorical or already grouped into ranges. In those cases, a categorical scatter plot, heatmap, or dot plot will convey the information more clearly.

For more design tips, visit our scatter plot resource page.

Connected scatter plot

What is a connected scatter plot?

A connected scatter plot begins exactly like a regular scatter plot: two numerical variables go on the x‑axis and y‑axis, and each dot represents one observation. After plotting the dots, you draw a line that links them in sequence. The line links the points in order. However, each point stays visible, so you can read both the individual observations and the overall trend.

When should you use a connected scatter plot?

Choose a connected scatter plot only when you have two numerical variables that change in a clear sequence, such as year‑by‑year data. The line shows how one variable moves as the other shifts, letting you highlight dramatic moments. Keep the point count low and the order unmistakable. If a standard line chart or a pair of separate lines can show the story just as well, use those simpler options instead.

When you shouldn’t use a connected scatter plot

Most readers find connected scatter plots hard to interpret unless you add plenty of explanatory annotations. If the same story can be told with a regular line chart, multiple lines, or a plain scatter plot, choose those simpler options instead. Also, avoid the connected version when the data has no clear order or when a large number of points would make the path cluttered and misleading.

Hexagonal binning

Alternative names: hexagonal plot, hexagonal bin plot

What is hexagonal binning?

Hexagonal binning is a heatmap of point density made with hexagons instead of squares. Lay a honeycomb over the x–y plane. Each cell counts how many observations fall inside, and the cell’s color shows that count (or a normalized density). Darker means more, lighter means fewer. Hexagons have six equal neighbors, which helps reduce grid artifacts and produces a smoother pattern. This makes clusters and gaps easier to see in very dense data.

When should you use hexagonal binning?

Use a hexagonal binning chart as a replacement for a crowded scatter plot. When thousands of points overlap, hexbinning reveals where data concentrate and where gaps exist without the noise of over‑plotting.

When you shouldn’t use hexagonal binning

Think twice if your audience is not used to reading hex‑bin charts. Without a clear legend that links color to counts, many readers will be unsure how to interpret the picture. Because binning replaces points with aggregated counts, it hides exact values and can mask rare outliers. So this makes it a poor choice for small samples or analyses that depend on precise numbers. Also, the result is very sensitive to bin size. If you set the hexagons too small, the display looks noisy. If you set them too large, meaningful detail disappears.

Creating a good hex‑bin plot also takes more skill and software support than drawing a standard scatter plot. You need a tool that supports hexagonal tessellation, and you need experience to choose an appropriate bin width and color scale. If a simpler chart, such as a plain scatter plot, can tell the story clearly, it is usually the better option.

Contour plot

What is a contour plot?

A contour plot shows three numerical variables on a flat page. Two variables form the x‑axis and y‑axis, just like a scatter plot. The third variable appears as curved lines that link every point with the same value. Picture a topographic map: one line marks 200 m above sea level, the next marks 300 m, and so on. Wherever the lines crowd together, the value changes quickly, and where they spread apart the surface is flatter. If you fill the spaces between the lines with colour, the plot reads like a smooth relief map of the data.

When should you use a contour plot?

Contour plots shine when you need to show a continuous surface. For example, elevation on a landscape or temperature across a weather map. Their flowing lines help readers see gradients quickly. This is why they are common in cartography, meteorology, oceanography, and other scientific fields that map smooth phenomena such as density, pressure, or magnetic strength. However, they can also suit business or retail data if you want to highlight dense clusters without clutter.

When you shouldn’t use a contour plot

Avoid a contour plot if the audience is not used to reading contour lines. It takes practice to translate the spacing of the lines into a sense of magnitude. The method also assumes that the data vary smoothly. If the surface is patchy or if there are sharp jumps, the interpolated lines can be misleading. Contour plots require enough data to create reliable interpolation, so they are not suitable for small or widely spaced samples.

Finally, if a simpler chart such as a heatmap can communicate the story clearly, prefer the simpler option for ease of interpretation.

That’s it for correlation charts. In the next part we’ll explore part-to-whole & hierarchical charts. See you soon!

You can also check all other parts here:
Part 1: Comparison charts
Part 3: Part-to-whole charts
Part 4: Data-over-time charts
Part 5: Distribution charts
Part 6: Geospatial and other charts

Recently we’ve rewritten our essential article about 80 types of charts. Our main goal was to make the information structured and helpful right away. We talk about every chart starting from the look and feel and then going into practical advice on when you should and when you shouldn’t use this particular chart type. We will start publishing parts of this article here. One chart group per each article.

In the first part, we’ll talk about comparison charts. Comparison charts are usually the first choice for data visualization. This is because in most cases we visualize data to compare. Comparison charts are designed to show how values compare to each other. In most cases, a bar chart is the first and most logical choice.

Let’s start exploring comparison charts one by one. Keep on reading!

Bar chart

Alternative name: Bar graph

What is a bar chart?

One of the most common chart types out there. A bar chart is a set of rectangles with a length proportional to the values it represents. Each rectangle — the bar, is a representation of one category.

When to use bar charts?

Use bar charts when you need to compare values that correspond to categories or discrete values. Bar charts work best when visualized values are in the same order of magnitude.

Bar charts are universal. It can be used everywhere, from a school project to a scientific paper. It is widely recognisable. Bar charts are commonly used due to their simplicity. Viewers need just to decode their bars’ length and position, making bar charts very easy to understand. The general public is fairly capable of reading bar charts, so no extra dataviz expertise is necessary. The differences in bar length are easier to perceive, than, for example, differences in size and color. For this reason, bar charts are doing their job well. You should consider using bar charts in your data visualization if the data structure and the actual message allow for it.

When you shouldn’t use a bar chart

The bar chart has a few limitations. First, the axis along bar charts should always start from zero. It makes it impossible to use it for some specific data range that does not include zero. Also, if your values vary greatly in size, it’s better to use charts that use area for representing a value. Alternative options here are an icon chart or treemap. If there are a lot of bars (above 30), it becomes really hard to read the chart.

If you want to improve your dataviz skills and design the best bar chart you can check our bar chart resource page and discover even more pro design tips.

Column chart

Alternative names: Column graph, Vertical bar chart

What is a column chart?

Long story short, you can say that a column chart is the same thing as a bar chart, turned by 90 degrees. Indeed, a column chart is a type of chart that resembles a bar graph with bars positioned vertically.

When should you use a column chart?

The main difference between a column chart and a bar chart is in the usage of categorical labels. Long labels don’t suit column charts because of easy overlapping. But it might be useful if the labels are short and don’t take up a lot of horizontal space.

Column charts can sometimes be useful when showing date-over-time data. But there is a limitation. It only works if there are only a few timestamps. For an extended number of timestamps, it’s better to use line charts.

Column charts are as universal as bar charts and can be used in every domain where charts can be placed. Column charts are better than bar charts for cases when categories have a natural order, like income levels or education levels. Column charts are very easy to read and they are easily recognised by the general public.

When you shouldn’t use a column chart

If the names of the categories are too long, column chart becomes really cluttered and hard to read. Changing the angle of the labels won’t solve the problem, it makes labels unreadable. So if the labels are too long, use bar chart.

You can use our bar chart resource page to learn how to improve the readability of your column chart as well.

Grouped bar/column chart

Alternative names: Paired bar/column chart, Clustered bar/column chart

What is a grouped bar chart?

A grouped bar chart is a multi-series variation of a bar chart where every category is represented by several columns. Each bar is communicating different aspects of the main category. Columns of each category are separated from the other categories using spacing.

When should you use a grouped bar chart?

This type of chart is used to compare multiple bars based on two variables, either categorical or discrete. For example, product sales grouped by product categories. Usually these types of charts are used in reports or presentations. If you need to make a comparison of values that are related to multiple categories and they should be on the same line, use a grouped bar chart.

When you shouldn’t use grouped bar chart

Despite this chart being a variation of a very useful and popular bar chart, it comes with a few guidelines to keep in mind. Placing too many categories makes the chart cluttered and hard to read. Also, there are a lot of cases when poor color choice makes grouped bar charts unreadable.

Lollipop chart

Alternative name: Lollipop plot

What is a lollipop chart?

This type of chart consists of a line and ends with a dot, or a circle. The length of the line represents the value. The dot or a circle used as an extra highlight of the value.

When should I use a lollipop chart?

You can use a lollipop chart when you need to show the relationship between a numeric and a categorical variable.

A lollipop chart can be a sweet alternative to a regular bar chart if you are dealing with a lot of categories and want to make optimal use of space. All the rules that are applied for bar and column charts can also be applied here.

When you shouldn’t use a lollipop chart

When you have enough space for a bar chart, it is better to use a bar chart. Also, when you have values that are very close to each other, bar charts are better. They are easier and more accurate to read than a lollipop because of the data mark that can be hard to compare, especially when the data mark is big.

Bullet chart

Alternative name: Bullet graph

What is a bullet chart?

A bullet chart is a type of chart designed to benchmark against a target value and ranges. Bullet charts resemble a combination of bar/column charts and progress bars. The results are shown in a single bar or column. The ranges bar is constructed based on values from a category that comparison will be based on (for example competitor sales figures). All these values are then divided into a certain number of sub-ranges (in most cases it’s quartiles). Target shows the value which is aimed for. And the bar shows the actual figures.

When should you use a bullet chart?

Bullet chart is a space-efficient chart used primarily for displaying performance data. Bullet chart is a primary choice for cases where multiple specific values should be shown in a compact way. Actual value, target values, reference ranges — all this can be placed in a space of a single bar.

When you shouldn’t use a bullet chart

Bullet charts have limited use. Being a very specific performance or benchmarking chart, is not useful for any other cases.

Dot plot

Alternative name: Dot chart

What is a dot plot?

Dot plot consists of one or more dots related to each category.

When should you use a dot plot?

Dot plots are a good choice for comparison. Especially if you need to place multiple values under one category. It allows you to have a lot of information on the chart and be clean and clear at the same time. Also, it’s important that it can be used for ranges of values. Unlike bar charts, you don’t need the X axis to start from zero. It gives additional freedom for changing the chart range. It allows you to zoom into a certain part of the dataset.

Dot plot packs a lot of information in a small space. A dot plot with only one value per category makes a comparison between those categories very easy. When the dot plot has multiple values per category, you can also compare within the categories.

When you shouldn’t use a dot plot

This chart may need gridlines that turn a dot plot into a chart with a proper context. Visually it might lack some “base”, compared to bar charts.

More about dot plot

We wrote a very interesting article about dot plots.

Make sure to also check our dot plot resource page and discover pro design tips.

Dumbbell

Alternative names: Dumbbell plot, Dumbbell chart, Connected dot plot, Dumbbell dot plot, DNA chart, Barbell chart

What is a dumbbell chart?

A dumbbell is a variation of dot plot with two connected values per category. A dumbbell consists of dots (or circles) and connectors (or lines). Not adding marks and only leaving the connector makes it a range chart.

When should you use a dumbbell chart?

Use it when you want to emphasize the delta (change) between the two values (data points, i.e. two points in time). Also it can work for comparing values in the same category and throughout all the categories of the chart.

A dumbbell chart is a good choice for visualizing paired values. For example, gender pay gap.

When you shouldn’t use a dumbbell chart

Dumbbell charts work for very specific cases of paired values per category. If it’s not the case, this chart makes little sense.

Pictogram

Alternative names: Pictorial chart, Proportional unit chart, Picture graph

What is a pictogram chart?

A pictogram chart is a type of chart that uses icons or symbols, or even small images, to represent data. Each of these icons can correspond to a certain category. Another option is that the icon represents a certain value or a visual representation of an item. Pictogram charts to some extent resemble bar charts, but instead of using a bar, they show icons.

When should you use a pictogram chart?

Some data visualization experts might argue this type of chart is very basic, to the point that it’s widely used in schools and kindergarten. But it’s important to keep in mind that using a pictogram chart helps overcome language barriers and it’s easy to interpret.

The simplicity of the pictogram chart allows it to use it in cases when the audience is not prepared for charts. IKEA is using only images in their assembly diagrams which makes them universal for all languages. It’s not a dataviz example, but it shows how icons can be a powerful tool for multi-language communication.

When you shouldn’t use a pictogram chart

If the audience of the chart are professionals that constantly work with data, using pictogram charts might be an oversimplification.

Icon chart

Alternative name: Proportional area chart

What is an icon chart?

The icon chart consists of icons of a different area. Each icon corresponds to one category. For most cases the placement of icons can be customized.

When should you use an icon chart?

Icon charts are an effective alternative to bar charts when there’s a significant difference between the values. The icon chart uses area rather than length to visualize values, which allows it to display a larger range of values in a compact way.

When you shouldn’t use an icon chart

Icon charts can be tricky to read. Usually readers are not used to perceiving area in correspondence with the value. It can be hard to see the difference if the values have minor differences. Keep that in mind, if you’re planning to use an icon chart in your visualization.

Range plot

Alternative name: Range chart

What is a range plot?

The range plot looks similar to a bar chart. It also consists of rectangular bars. The difference is that a range plot shows two values of a category, instead of just one. These two points are connected with a line or a bar between them. This line indicates the difference, or a gap, between these points and suggests a direction of such change.

When should you use a range plot?

Use a range plot if you want to highlight the difference between two values, rather than showing these values as something separate. A use case example is any sort of demographic gap, i.e. gender pay gap. Compared to the dumbbell chart, the range chart is more focused on the range itself rather than the individual data points.

When you shouldn’t use a range plot

When you want to focus on a certain value related to a category, better to choose another chart, like bar chart or icon chart.

Radial bar chart

Alternative name: Circular bar chart

What is a radial bar chart?

A radial bar chart is a variation of a regular bar chart with the main difference being the circular shape of the chart. The chart itself is plotted on what is called a polar coordinate system. It means that each bar appears in a circle. The larger the value, the longer the bar.

When should you use a radial bar chart?

Try radial chart if you really want to grasp the attention and you want to try something fancy. Also, if the accuracy of data presentation is not your first priority, it is fine to use it.

When you shouldn’t use a radial bar chart

I was always puzzled by this chart and inability to show the values in a logical way. So let me know if you find a legitimate use case for this chart. It is really hard to read and interpret. Inner and outer bars can seem totally different in size and visualise the same value. Inner bars tend to be shorter than the outer ones.

Parallel coordinates

Alternative names: Parallel plot, Parallel coordinates plot

What is a parallel coordinates plot?

The parallel coordinates chart resembles a line chart. Instead of time values, categories are plotted on the horizontal axis.

When should you use a parallel coordinates plot?

In case you need to plot a multitude of categories/dimensions without compromising the readability in a simple 2d space. Every dimension can be presented with a separate axis and each of them can have its own range.

When you shouldn’t use a parallel coordinates plot

The relative simplicity of the chart, yet, adds some limitations. If each dimension has its own scale, just two neighboring dimensions relationships can be followed at a time. You might look at it as a chain of slope charts. Ordering plays a crucial role in this chart. This chart is not an easy one to read, so you need to consider if the data can be presented in some other way.

Radar chart

Alternative names: Spider chart, Spider graph, Web chart, Spider web chart, Star chart, Star plot, Cobweb chart, Irregular polygon, Kiviat diagram

What is a radar chart?

A Radar chart consists of several axes, all coming from the same point in the center (which resembles a spider web). Every axis is connected to a specific attribute of the compared items (the minimum number of attributes is three).

When should you use a radar chart?

Radar charts are used for making a visual “profile”. Each axis corresponds to an attribute. Usually radar charts are used for side-by-side comparison on the same chart or using separate charts for each profile. They useful for getting an approximate picture.

When you shouldn’t use a radar chart

Although it’s a very interesting chart to use, it’s important to keep in mind that it is hard to read. As it is designed in a circular fashion, it requires extra visual perception, in contrast to the more common linear types of charts and graphs. It is often easier to replace it with another type of chart. If all axes in your chart have the same scale, then a bar chart or sometimes a lollipop will suffice. If the axes have a different scale, it’s better to use parallel coordinates.

Nightingale chart

Alternative names: Nightingale’s graph, Nightingale rose chart, Rose diagram, Coxcomb chart, Polar area chart

What is a Nightingale chart?

A Nightingale chart is a variation of a pie chart. It also consists of sectors, but with different radiuses. In this chart, the value corresponds to the area of the sector, not the radius.

When should you use a Nightingale chart?

A Nightingale chart is visually similar to the pie chart, but it has a different function. It’s used mostly for comparison.

When you shouldn’t use a Nightingale chart

In most cases, a Nightingale chart is not the best primary chart choice. It’s really hard to compare the sector areas of a Nightingale chart precisely. It also inherits all the issues of the pie chart. The most obvious is that it’s hard to distinguish sectors with similar values. For these types of cases, it’s better to use bar charts.

Waterfall chart

Alternative names: Flying bricks chart, Mario chart, Bridge chart, Cascade chart

What is a waterfall chart?

In the waterfall chart, the first value (column) typically starts from the baseline of zero, as does the ending value. They are connected by a number of seemingly floating shorter bars that show the intermediate changes. The overall shape of the chart resembles a waterfall.

When should you use a waterfall chart?

A waterfall chart is a type of graph that usually shows positive and negative values of change between two points, which helps in understanding the cumulative effect of these changes (so the net change).

This chart does not only look at the starting value and the ending value of your data set but also shows each individual positive or negative change that happened. As you can imagine, this type of chart is quite useful in financial sectors or human resources, but also in other industries (think of inventories, revenue tracking, etc.).

When you shouldn’t use a waterfall chart

A waterfall chart is not useful if you want to visualize and communicate the absolute values of each parameter, not the influence of this parameter on the whole.

Matrix chart

Alternative name: Matrix diagram

What is a matrix chart?

A matrix chart is a grid of two or more categorical variables. On the intersection of the categories one or more icons are placed, showing the type of the relationship between categories.

When should you use a matrix chart?

A matrix chart is a chart that helps in visualizing the relationship between two or more categorical items. Specifically, it shows the presence and strengths of such relationships. It can have different forms depending on how many groups must be compared.

When you shouldn’t use a matrix chart

You should be careful if you need to display more than two categorical dimensions, as adding more can quickly reduce readability.

Small multiples

Alternative name: Trellis chart, Lattice chart, Panel chart

What is a small multiples chart?

Unlike all the other graphs in this article, small multiples are more of a visualization concept than a graph itself. This is because small multiples repeat the same type of chart in a grid to show different slices of the dataset.

When should you use small multiples?

The main advantage of using small multiples is the possibility of showing three or (usually) more variables presenting different values in the same graph without confusing your audience. The main way of using small multiples is to split charts that are too complicated or cluttered into many simple charts. For example making a small multiple chart out of a spaghetti chart (line chart with too many lines. Another example is making small multiples out of stacked or grouped bar charts.

When you shouldn’t use small multiples

Small multiples work best when there is enough space to show each chart clearly. If space is too limited, lose clarity. Also, if a single chart can show the same story, they are not needed.

Word cloud

Alternative name: Tag cloud, word collage, wordle

What is a word cloud?

A word cloud is a visual cluster of words that vary in size based on how often they appear in the dataset. In other words, the more frequently a word or keyword appears in the text, the larger and often bolder it will look in the cloud.

When should you use a word cloud?

Word clouds can be helpful for analyzing data related to language and narrative studies. It allows you to get a fast approximate overview of the word patterns in a text.

When you shouldn’t use a word cloud

Word clouds only give a rough idea on the frequency of the words within the dataset. The exact area of the word is not always corresponding to the value because it’s really hard to calculate the area precisely. Words have a complex form and it also depends on the font that is used.

Slope chart

Alternative name: Slopegraph

What is a slope chart?

A slope chart consists of two vertical lines representing start and end points. Data points are placed on each of these lines representing values for one category. Two data points are connected with a line, showing the change using the slope of the line.

When should you use a slope chart?

A slope chart can be used as a replacement for a line chart, grouped or stacked bar chart. If there are only two values to compare, it works perfectly.

When you shouldn’t use a slope chart

If there are more than two time or measuring points, the slope chart won’t work. In this case more conventional chart types like variations of bar chart or line chart should be used.

Table chart

What is a table chart?

Table chart is a usual table. It includes the usual attributes, like rows and columns of data. The difference is that the table chart has some additional visual elements that allow it to highlight certain cells. The most popular version of table charts is a table with some condition formatting. It can look almost like a heatmap, but the difference is that every column(s) or row(s) can have a different rule of coloring.

When should you use a table chart?

Table charts are a good starting point for exploiting the data. Similar to scatter plots and heatmaps, it gives an overview of the data. Sometimes it can also work as a final presentation visualization.

When you shouldn’t use a table chart

The possibilities of using different rules for different columns and rows gives not only the freedom of exploration but also might cause confusion for the reader. Table charts are usually not self-explanatory and need some additional notes and guides on where to look at and pay attention to.

Categorical scatter plot

What is a categorical scatter plot?

A categorical scatter plot differs from a regular scatter plot by the presence of a categorical axis. It can be just one categorical axis or both of them.

When should you use a categorical scatter plot?

Categorical scatter plots can be useful for visualizing a set of 2 categorical variables and one numerical variable. An example is visualizing the results of polls. A bubble positioned at the intersection of two categories shows the numerical value that is related to that specific category pair.

When you shouldn’t use a categorical scatter plot

Categorical scatter plots can have limited utility if the number of categories along each axis exceeds 20–30. In this case the representation of the values can be perceived only approximately. At that point, the scatter plot doesn’t add much value compared to just using a regular table.

Quadrant chart

Alternative names: matrix diagram, matrix chart, 4-quadrant matrix chart

What is a quadrant chart?

A quadrant chart is very similar to a scatter plot but it’s divided into equal parts (quadrants) in a matrix view. It can be 2x2, 3x3, 4x4 or other ratios, depending on the aim of the visualization.

When should you use a quadrant chart?

It is useful if you want to group distinct data marks for some specific type of analysis. One of the best and most well-known examples of using the quadrant chart is for a SWOT analysis. The quadrant chart is useful for benchmarking if there are two dimensions that should be taken into account.

When you shouldn’t use a quadrant chart

In case the splitting into quadrants doesn’t make sense or looks forceful.

That’s it for comparison charts. In the next part we’ll explore correlation charts.

You can also check all other parts here:
Part 2: Correlation charts
Part 3: Part-to-whole charts
Part 4: Data-over-time charts
Part 5: Distribution charts
Part 6: Geospatial and other charts

Histograms and bar charts look almost identical. If you have ever stared at your data trying to decide which of the two to use, or realized too late that you picked the wrong one, you are not alone.

Many of us were introduced to histograms in a classroom setting, which made them feel abstract and intimidating. But in everyday work, they can be vital.

Take delivery times, for example. If you want to see if packages are arriving consistently or if delays are random, a bar chart will not tell the whole story. You need a histogram to see the spread.

In this article, we will clear up the confusion. By the end, you will know exactly when to use which chart to tell the best story with your data.

Part 1: The Experiment (Why Bar Charts Fail on Continuous Data)

Let’s start with a test with some fictional data. Imagine you have a dataset of 100 package deliveries. You want to answer a simple question: “What is our normal delivery time?”

Your data isn’t simple categories like “Truck A” or “Truck B.” It is specific, flowing time measurements. You have a list of Package IDs (#1 to #100) and the exact hours it took for each one to arrive.

If you instinctively throw this raw data into a Bar Chart, you are going to get a mess.

(Note: The datasets and charts shown below are fictional and created for demonstration purposes.)

Because the bar chart treats every single Package ID as its own distinct category, it draws 100 separate bars standing next to each other.

It looks like a broken comb or a barcode. Is 52 hours normal? Are there a lot of delays? It is impossible to say.

This is technically a chart, but it is practically useless. It fails because it focuses on the items (Package #1, Package #2) rather than the pattern .

Part 2: The Fix
(Enter the Histogram)

To make sense of this, we need to stop looking at the individual packages and start looking at ranges.

We group the delivery times into buckets (also called “Bins”). Instead of plotting 100 individual bars, we count how many packages fell into specific time slots (e.g., 24–27 hours, 48–51 hours, etc.).

Suddenly, that messy “broken comb” transforms into a clear, solid shape.

(Note: The datasets and charts shown below are fictional and created for demonstration purposes.)

Now you can see the story:

• The peak: You can instantly see that the most common delivery time is between 48 and 51 hours (with 25 packages landing in this bin).
• The spread: The majority of your deliveries happen consistently between 44 and 55 hours.
• The outliers: You can easily spot the exceptions. Very few packages took longer than 60 hours, and none took longer than 67.

This is the superpower of the Histogram: It sacrifices specific details (we don’t know exactly which package took 67 hours) to show you the big picture (the trend).

Part 3: What a Bar Chart Is Actually Good At

Now that we have seen what happens when you try to force continuous numbers into a bar chart, let’s take a step back and look at what a bar chart is designed to do. When your data fits the chart, a bar chart is one of the clearest and most reliable ways to communicate a message.

At its core, a bar chart is a group of rectangles whose lengths represent values. Each bar stands for one category. This simple idea is what makes bar charts so effective. Our eyes are very good at comparing lengths, which is why differences between categories are easy to spot.

Bar charts use gaps between the bars to show that the categories are separate. The x-axis lists the categories, and the y-axis shows a numeric value such as a count or a total. This setup makes the chart straightforward for almost anyone to read.

Bar charts often work best in a horizontal layout, because there is more space for the category labels. Sorting the bars from high to low or low to high also helps readers understand the differences quickly. Sometimes categories already follow a natural order, such as days of the week or age groups. In that case, it is better to keep the natural order and use the vertical version, the column chart.

Color is most effective when used in a simple way. One color is enough for most bar charts. If you want to highlight something important, you can use one accent color to draw attention without overwhelming the rest of the bars.

For labeling, direct data labels often work better than relying on the y-axis and gridlines. This keeps the chart clean and helps the reader focus on the values.

When to use it:

Use a bar chart when your data contains clear categories and your message is about comparing those categories.

Part 4: What a Histogram Is Actually Good At

Now that we have seen why bar charts struggle with continuous data, let’s look at the chart that is built for it. When your goal is to understand the shape of your data, a histogram is one of the clearest and most informative charts you can choose.

At its core, a histogram also uses rectangles, but with a different purpose. Each bar represents a range of values rather than a single category. These ranges are called bins. The height of each bar shows how many data points fall inside that bin, the frequency. This simple grouping is what allows a histogram to reveal patterns and variation in continuous data.

Histograms have no gaps between the bars, because the bins follow each other along one continuous scale. The x-axis shows the numeric scale divided into these ranges, and the y-axis shows how many times values fall within each range. Together, these elements reveal the structure of your data in a way a bar chart cannot.

Histograms work best when the bars follow the natural order of the numeric scale. You cannot sort the bins in another way, because the order is part of the meaning. The shape comes from the data itself, not from the categories.

Color works best when kept simple. One color is usually enough, because the goal is to help the reader focus on the shape of the distribution. If you want to highlight something important, you can use one accent color to draw attention to a specific bin without distracting from the overall pattern.

Labeling also works differently. Direct labels do not work well in a histogram, because the bars sit close together and form a continuous distribution. There is also rarely a need to show the exact value of each bin, since the goal of a histogram is to reveal the overall shape of the data. Clear axis labels and a few gridlines are usually enough for readers to understand the frequency and the pattern.

When to use it:

Use a histogram when your data sits on a continuous numeric scale and your message is about the distribution. A histogram helps you understand what is common, what is rare and how values spread across the range.

Part 5: Choosing the Right Chart

By now you have seen both charts in action and how differently they behave. To make the choice even clearer, here is a simple side-by-side overview of the most important differences. Use it as a quick guide whenever you are unsure which chart fits your data and your message.

A bar chart is the best choice when your data consists of clear, separate categories and your goal is to compare those categories. A histogram is the best choice when your data sits on a continuous numeric scale and you want to understand the distribution.

The table below shows how both charts differ in structure, purpose and design. Each row highlights one aspect that can influence your decision.

Part 6: Best Practices for Using a Histogram

A histogram is a powerful chart, but only when it is designed with care. Because it is less common than a bar chart, many readers need a little extra guidance to understand what they are looking at. The practices below will help you create histograms that are clear, accurate and easy to interpret.

Use histograms carefully when sharing insights

A histogram is great for your own analysis because it helps you explore patterns and detect variation in the data. When you present a histogram to others, think about whether your audience is familiar with this type of chart. Some readers may not immediately understand that a histogram groups values together and that the bars represent ranges. If you choose to include one, make sure the context is clear and the viewer knows why the histogram is the right tool for the question.

Give a short explanation of what the chart shows

Because histograms are less common, a quick explanation helps a lot. Clarify that the x-axis is divided into bins and that each bar contains multiple data points. This prevents viewers from reading the histogram like a bar chart with separate categories. Even a simple sentence can make the structure clear and help your audience interpret the distribution correctly.

Choose a vertical layout

Although a histogram can technically be rotated, the vertical layout works best. It reinforces the natural flow of the numeric scale from left to right and makes it easier for readers to follow the progression of the bins. A horizontal histogram is possible, but it often hides this natural order and can feel less intuitive.

Use clear axis labels

Axis labels are essential in a histogram.

• The y-axis should clearly state that it shows a frequency.
• The x-axis should make it obvious that the values are grouped.

Labels such as “Delivery time (hours, grouped in 4-hour bins)” help the reader understand why one bar represents multiple data points and what the ranges mean.

Choose the right bin size

Bin size has a strong impact on the shape of the histogram.

• Large bins give a broad overview.
• Small bins reveal more detail.

There are formulas that estimate a good bin size, such as Sturges’ rule:

K = 1 + 3.322 log(n)

Where K is the number of bins and n is the number of observations.

Sturges’ rule is used in many tools but is sometimes criticized for smoothing the data too much. In practice, it often works to start with an estimated bin size and then adjust it to match the logic of your data and the message you want to tell.

Part 7: Two Examples That Show the Difference Clearly

Sometimes a single example is not enough to show why bar charts and histograms behave so differently. Below are two short scenarios that use the same dataset in two different ways. In each case, the bar chart and the histogram answer a different question. This makes it easier to see when to choose which chart.

(Note: The datasets and charts shown below are fictional and created for demonstration purposes.)

Example 1: Website Session Duration

This dataset contains the duration (in minutes) of 60 website sessions, along with the device used. Sessions range from just a few minutes to nearly an hour.

session_id device duration_minutes
1 mobile 3
2 desktop 12
3 mobile 7
4 tablet 4
5 desktop 22
6 mobile 1
7 desktop 9
8 mobile 15
...
(60 rows total - fictional sample data)

How the data transforms into a bar chart

To create a bar chart, you need categories. That means reorganizing the data so each device type becomes one bar. You can do that by creating a pivot table that counts how many sessions came from each device. Now your chart is ready, because each device is a clean, discrete category.

It answers the question: Which device type accounts for most sessions? This is a pure category comparison. It works because “mobile”, “desktop” and “tablet” are discrete groups.

How the data transforms into a histogram

For a histogram, categories are not useful. You need ranges, not device types. You can do that by creating 5-minute bins (0–4, 5–9, 10–14, etc.). Then count how many sessions fall inside each bin. This converts 60 individual durations into a frequency distribution.

It answers the question: How long do sessions usually last? You see patterns in the spread: a cluster of very short visits, a group around 5 to 15 minutes, and a long tail of longer sessions.

Example 2: Coffee Shop Sales

This dataset contains 20 receipts from a fictional coffee shop. For each receipt you see the payment method and the total amount spent (in euros). The values range from small purchases under €5 to higher amounts above €10.

receipt_id payment_method total_eur
1 card 4.8
2 cash 6.1
3 card 3.9
4 mobile 5.2
5 card 7.4
6 cash 9.8
7 card 4.5
8 mobile 10.2
...
(80 rows total - fictional sample data)

How the data transforms into a bar chart

To create a bar chart, you again need categories. Here, “payment_method” is the clean categorical field. To prepare the data, create a pivot table that counts how many receipts were paid by each method.

Your new dataset becomes something like:

• card: X receipts
• cash: Y receipts
• mobile: Z receipts

Now your chart is ready, because each payment method is a clear, separate group. The bar chart answers the question: “Which payment method is used most often?”

It reveals something about customer behavior. Maybe card payments are dominant, mobile payments are growing, or cash is declining. These are category-level insights you cannot see in the raw numbers.

How the data transforms into a histogram

For the histogram, you ignore the payment methods entirely. Instead, you look at the amount people spend. To prepare the data, group the totals into bins such as: €0–4, €4–8, €8–12, €12–16. Then count how many receipts fall into each range.

The histogram answers a different question: “How much do customers typically spend?”

It shows the shape of the spending distribution:

• a cluster around €4–6
• another around €6–10
• a few higher purchases above €10

This gives you a sense of what is “normal” spending and how wide the spread is.

The Final Takeaway: Trust the Shape of Your Data

Histograms might bring back memories of dusty textbooks and strict statistics rules. But as we have seen, they do not have to be scary. They are simply a tool to help you see the shape of your data. A shape that a bar chart often hides.

While bar charts are great for declaring “winners” (like which carrier is fastest or which device is most popular), histograms are necessary for understanding “reliability” and “normality”. They answer questions like: How consistent are we? What is the typical range?

The next time you are staring at your dataset and fighting the urge to just pick the chart that looks the prettiest, ask yourself one simple question: “Am I comparing distinct groups, or am I looking for a trend in the numbers?”

• If it is distinct groups, give them space. Use a Bar Chart. Start making bar charts right now with our bar graph maker.
• If it is a trend in the numbers, let them flow. Use a Histogram. You can start right now with our histogram maker.

Mastering this distinction is a small technical step. But it makes a huge difference in how clearly you communicate your work. And hey, maybe now those statistics class memories will not feel quite so alarming.

Once you know which chart you need, you can start shaping it to match your story. Datylon for Illustrator makes this part simple. You can remove the gaps to turn a bar chart into a true histogram, adjust the x-axis to show your bins clearly, restyle the y-axis and gridlines, or add prefixes and suffixes to your labels. Once your design is set, you can reuse the same chart as often as you like by updating the data. And if you want to go one step further, you can even automate it with our tools.

Originally published at https://www.datylon.com.

You know that spark when a chart tells you more than words ever could? I’ve gathered ten standout visualizations, each one remarkable not only for its design but for the real impact it made. Some may not be the most visually stunning but helped make data viz a household term, others are beautiful and actually drove change. In the sections ahead, you’ll learn why each visualization earned its place on this list and what we learned from each.

Rose Diagram by Florence Nightingale (1858)

Florence Nightingale’s rose diagram (or “coxcomb chart”) was groundbreaking in the 1850s. She plotted monthly deaths as wedges whose area, not just length, corresponds to mortality. She turned dry hospital records into a striking visual. And the result wasn’t just beautiful. It revealed that most soldiers died of preventable diseases, not battle wounds. This stimulated vital sanitary reforms.

⭐ Why it’s in my top 10

• Innovative use of area: Nightingale didn’t rely on bars or lines. Instead, she mapped each month’s death toll to the area of a wedge. This lets viewers instantly compare magnitudes. Large wedges jump off the page, making spikes in mortality crystal clear.
• Engaging aesthetics: The petals of this “rose” chart aren’t decorative fluff. Their graceful shape and distinct color bands guide your eye through the data. By marrying form with function, Nightingale made statistics feel human. This draws in audiences who might otherwise ignore medical reports.
• Perfect for seasonality: Months loop naturally in a circle, and this design mirrors our sense of time’s cycle. You sense winter peaks and summer lulls without reading labels. Seasonal patterns are immediately apparent. This emphasizes the importance of disease prevention during the harsh months.
• Clear year-to-year comparisons: By placing two concentric roses for different years with a subtle connector line, she showed progress over time.

💡 What we can learn from this data visualization

Nightingale’s rose diagram persuaded policymakers to reform hospital sanitation. This proves the power of purpose-driven design. Your chart can do the same: first, define the action you want your audience to take. Next, highlight that insight with size, color contrast, or layout. Finally, add a clear label or note explaining its significance and the steps viewers should follow.

Flow Map of Napoleon’s Invasion of Russia by Charles Joseph Minard (1869)

When I asked my colleague Ivan what his favorite data visualization is, he immediately pointed to this map. He said: “For me, Charles Minard’s March Map stands out as a top choice. It’s an incredibly well-thought-out and meticulously crafted visualization. This piece blends various chart forms in a graceful manner. You can grasp its essence immediately, yet it offers intricate details for extended exploration.”

Charles Minard’s 1869 flow map visualizes Napoleon’s 1812 Russian campaign in a single image. The thick tan line shows the Grande Armée’s advance toward Moscow, with line width proportional to troop numbers (1 mm = 10 000 soldiers). As the army retreats, a black line traces its path home, again scaled by remaining troops. Along the bottom, a temperature plot (in degrees Réaumur) aligns with dates and locations on the retreat. By layering geography, time, direction, army size, and temperature, Minard’s chart tells a tragic story of war losses that no text alone could convey.

⭐ Why it’s in my top 10

• Immediate storytelling: From the moment you see it, the map shows troops moving toward Moscow and then struggling to return home. You don’t need a legend to understand the advance and the retreat.
• Color as narrative: The thick tan line represents soldiers marching to Moscow, while the black line shows the survivors heading back. This simple color choice makes the tragic outcome unavoidable.
• Multivariate mastery: Minard’s map encodes troop numbers via line width, geographic locations on a map, and a temperature chart for the retreat. Layering six variables into one image turns data into a clear, coherent story.
• Timeless relevance: With conflicts still raging worldwide, this visualization reminds us that numbers on a page translate directly to human suffering. An urgent lesson that remains as important now as it was 150 years ago.

💡 What we can learn from this data visualization

As Edward Tufte wrote in (2001), Minard’s map is “possibly the best statistical graphic ever drawn.” Minard’s map shows how deeply data and narrative can work together. When you visualize complex information, weave a clear story through color, size, and sequence so viewers feel the impact and not just see the numbers. The Visual Display of Quantitative Information

Periodic Table by Dmitri Mendeleev (1869)

You might not think of the periodic table as a “chart,” but I think it’s one of the most powerful data visualizations ever created. By arranging every known chemical element in a grid based on atomic number and shared properties, it turns a mountain of raw information into a clear map of element behavior. Each cell shows an element’s symbol, atomic number, and atomic weight. Grouping elements with similar chemistry in columns (groups) and fitting trends across rows (periods) reveals patterns that help predict how elements will react.

⭐ Why it’s in my top 10

• Revolutionary organization. In 1869, Dmitri Mendeleev arranged elements so scientists could spot gaps and predict missing ones. This turned raw chemical data into a tool for discovery.
• Instant recognition. The grid is simple with symbols and numbers, but people around the world immediately understand it. You don’t need a chemistry degree to see the structure.
• Pattern revelation: Elements with the same number of outer electrons appear in the same column. That makes properties like reactivity or magnetism pop out. You can tell at a glance that alkali metals act very differently from noble gases.
• ​​Adaptable and enduring: Over 150 years later, we still add new elements and isotopes without breaking the table’s clarity. A well-planned grid can grow and stay useful.

💡 What we can learn from this data visualization

When handling complex data, look for natural groupings. By organizing related items into clear categories and ordering them logically, you help viewers spot patterns instantly. Whether you’re building a table, chart, or diagram, let the inherent structure of your data guide the layout so that key insights emerge without extra explanation.

Occupations of Negroes and Whites in Georgia by W. E. B. Du Bois (1900)

W. E. B. Du Bois and his students created over fifty infographics for the 1900 Paris Exposition. They broke new ground in both design and social advocacy. One standout example is the “Occupations of Negroes and Whites in Georgia” chart. Du Bois used two mirrored fan-shaped pie charts, one for Black Georgians and one for White Georgians. They compare the percentage working in agriculture, domestic service, professional roles, and other categories. Placing these fans side by side makes disparities and overlaps immediately visible.

⭐ Why it’s in my top 10

• Driving change with data. Du Bois used these charts to challenge racial stereotypes and advocate for educational and economic reforms. He believed that revealing the real numbers could spark action.
• Bold, ahead-of-its-time design. In 1900, the mirrored fan charts and bright color blocks were revolutionary. Du Bois’s choice of form and hue made the data clear, memorable, and impossible to ignore.
• Clean, minimalistic design with high data‐ink ratio. Du Bois stripped away unnecessary decoration, using direct labeling and simple shapes so every mark on the chart conveys information. This clean layout makes complex data immediately readable.

💡 What we can learn from this data visualization

Du Bois’s work shows that data visualization can be a force for good. By presenting clear, uncluttered charts that reveal inequalities, you make it impossible to ignore social problems. When you base your visuals on solid data and highlight disparities without clutter, viewers are more likely to understand the issues and feel compelled to act.

Animated Scatter Plot by Hans Rosling (2006)

Jumping ahead over a century from Du Bois’s 1900 fan charts, Hans Rosling’s 2006 animated scatter plot marked a major leap in time and in how we tell stories with data. In his TED Talk “The Best Stats You’ve Ever Seen,” Rosling brought data to life with a moving bubble chart. Income per person appears on the horizontal axis, life expectancy on the vertical axis, bubble size shows population, and color marks world region. As the chart plays through time-from 1900 up through 2006-you can see countries rise, stall, and catch up in just a few minutes.

Above is the same interactive chart Rosling used. Feel free to play with the time slider and watch history unfold.

⭐ Why it’s in my top 10

• Powerful storytelling: The animation turns decades of data into a story you can actually watch. You don’t just see start and end points, you follow each country’s journey year by year.
• Simple foundations: At its core it is just a scatter plot, so anyone can understand it. That makes the complex trends feel approachable.
• High engagement: Moving bubbles catch and keep your attention. Every shift invites you to ask “what happens next?”
• Functional animation: By revealing one year at a time, the chart prevents information overload. You can focus on each moment and understand how trends develop.

💡 What we can learn from this data visualization

Use animation only when it serves your story. First, decide what narrative you want to show, then animate the elements that help tell that story. Keep your transitions smooth and let viewers control playback so they can explore at their own pace. That way animation becomes a tool for clarity instead of just decoration.

Spurious Correlations by Tyler Vigen (2013)

Before I started working in data visualization, I vividly remember encountering a set of charts that were both hilarious and thought-provoking. Tyler Vigen’s Spurious Correlations project, launched in 2013, pairs unrelated time series to show that just because two lines move together doesn’t mean one causes the other. These charts grabbed my attention and taught me early on how easily data can deceive.

Tyler Vigen launched the project in 2013. He collected hundreds of simple plots where two unrelated time series happen to correlate almost perfectly. For example, the number of Nicolas Cage films released each year versus the number of people who drowned by falling into a pool. These examples demonstrate that a high correlation coefficient doesn’t imply any causal relationship. Sometimes unrelated trends simply move together by coincidence. Spurious Correlations

⭐ Why it’s in my top 10

• Instant engagement: The absurd pairings provoke a laugh and draw in anyone, even those with little data experience. That first smile makes people pay attention.
• Critical lesson: Each chart warns, “Just because two lines move together doesn’t mean one causes the other.” In an era of big data and clickbait headlines, this reminder is more urgent than ever. These charts become conversation starters. Viewers ask, “Why does that happen?” and learn to dig deeper into data sources and context before drawing conclusions.
• Wide appeal: The line chart format is familiar and easy to read. You don’t need advanced skills to see that the plotted points form a straight line. Yet no one believes Nicolas Cage causes pool drownings.

💡 What we can learn from this data visualization

Be vigilant with data and visuals. Always question if the numbers truly belong together. Check for hidden factors, and clearly explain any limitations so your audience isn’t misled by coincidence.

Poppy Field by Valentina D’Efilippo (2013 book; interactive version 2014)

Valentina D’Efilippo first published this chart as a static infographic in her 2013 book The Infographic History of the World. In November 2014, she and developer Nicolas Pigelet released an interactive version at poppyfield.org. The visualization represents major 20th-century conflicts as a field of poppies. Each poppy’s stem stretches from the year a war began to when it ended. The size of its petals corresponds to the number of deaths, and its color indicates the region where the fighting took place. Scrolling or hovering over a poppy reveals exact years and fatality figures. This turns raw war statistics into a living, unfolding tapestry of modern history.

⭐ Why it’s in my top 10

• Powerful symbolism: Poppies are instantly tied to World War I remembrance. By using a single, meaningful icon, the chart connects data to a broader cultural story.
• Broad appeal: The flower motif draws in readers who might shy away from charts. Even without data experience, you see a sea of poppies and feel the weight of wartime loss. Seeing a sea of poppies instantly evokes remembrance and reflection. The visual ties data to shared cultural meaning, making viewers pause and feel the human cost behind the numbers.
• Timely relevance: Although centered on 20th-century wars, its design resonates today. Ongoing conflicts remind us that the human cost of war endures. The long, overlapping stems immediately reveal which conflicts lasted longest, making duration clear at a glance.

💡 What we can learn from this data visualization

Use symbols or metaphors to create an emotional link between data and theme. A well-chosen icon binds people to the topic, makes the story memorable, and helps viewers grasp complex information quickly.

Warming Stripes by Professor Ed Hawkins (2018)

Climate warming is one of today’s most urgent issues, and no graphic captures it more powerfully than the “warming stripes. Each stripe represents one year’s average temperature: blue for cooler years, red for warmer ones. Laid side by side, they form a simple yet startling portrait of our changing climate.

On showyourstripes.info , anyone can generate their own regional stripes. You can turn global data into a personal story.

⭐ Why it’s in my top 10

• Instantly understood: You don’t need any technical background to read it: cool vs. warm is clear at a glance.
• Bold, accessible colors: The blue-to-red palette is vivid and, crucially, still distinguishable for most color-blind viewers.
• Risky minimalism, brilliantly executed: Relying solely on color might seem limiting, but here it makes the trend impossible to ignore. By stripping away axes, numbers, and labels, the warming stripes speak directly to our intuition: the planet is warming, and fast.

💡 What we can learn from this data visualization

Embrace minimalism: remove all non-essential details so that your visual elements alone convey the core message instantly and powerfully.

Try to Impeach This by Karim Douïeb (2019)

As we saw with spurious correlations, even familiar charts can mislead if you don’t question what they really show. That caution holds true for U.S. electoral maps, too. In September 2019, Lara Trump tweeted the classic 2016 choropleth. Every state painted solid red or blue by its winning candidate with the caption “Try to impeach this.” The map looked overwhelmingly red, but it hid the fact that most votes came from dense population centers. In response, Karim Douïeb launched “Try to Impeach This” ( https://try-to-impeach-this.jetpack.ai/). It is an interactive version that redraws each county as a dot sized by its actual voter population and colored by the winner. Scroll or hover, and you see instantly how “acres don’t vote-people do,” exposing the true electoral picture that Lara Trump’s original map obscured.

⭐ Why it’s in my top 10

• Critical topic: Electoral maps shape public perception, and misleading visuals can influence opinions. By exposing how standard red-blue state maps hide population density, this project tackles a timely issue in U.S. politics and media.
• Universal clarity: The simple toggle between area-based and population-based maps lets anyone, even without data-viz experience, see why the original map was flawed. Hovering over dots reveals precise vote numbers, so viewers understand the true electoral landscape without any jargon.
• Educational reach: Journalists, educators, and everyday readers have used this map to explain why “acres don’t vote, people do,”. This makes it a go‐to tool when discussing electoral geography.

💡 What we can learn from this data visualization

Always question whether your chart type and data truly reflect reality. Before you publish, ask: “Does this visualization show the whole story or could it mislead?” If you’re mapping results, consider using population-weighted designs or clear annotations so your audience sees the truth, not just a pretty graphic.

COVID- 19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (2020–2023)

During the COVID-19 pandemic, data visualization, particularly dashboards, experienced a surge in popularity. People sought to understand the rapidly evolving situation. A standout example that became popular, is the COVID-19 Dashboard by Johns Hopkins University . This dashboard rose to prominence due to its comprehensive approach. It draws data from a lot of sources to provide a global overview of the pandemic. Its widespread use meant that even individuals outside the data field became familiar with it. A quick online search for “dashboard” often surfaces images of this iconic interface.

Unfortunately, the dashboard is no longer active; data collection ceased on October 3, 2023.

⭐ Why it’s in my top 10

• Free data for all: All of its numbers, cases, vaccines, and deaths, were shared openly onGitHub . That meant students, researchers, and hobbyist programmers could download the data and build their own charts or apps. No sign-ups or fees required.
• Simple, clear view The main map shows each country in red for cases, green for vaccines, and white for deaths. Below or beside it are matching charts that track changes over time. The layout is clean and uncluttered, so even people new to data can see trends at a glance.

💡 What we can learn from this data visualization

Always design with your audience in mind. The Johns Hopkins COVID-19 Dashboard was built for the general public. It uses straightforward maps and simple time-series charts that anyone, from beginners to experts, can interpret at a glance.

If you’re creating a tool for specialist users, you can lean on more advanced or niche chart types. Your audience will appreciate and understand the extra complexity. Your audience’s understanding is the true measure of a good design.

Why These Visualizations Matter

Each visualization here did more than just display numbers. Some fueled reform or challenged injustice, while others reshaped how we think about data. For example, Nightingale’s rose diagram drove health reforms, Du Bois’s fan charts highlighted racial inequities, Minard’s flow map revealed the human cost of war, and modern tools like the electoral cartogram corrected misleading narratives. Meanwhile, the spurious correlations charts taught us to question causation, Rosling’s animated bubbles turned static stats into a living story, and the periodic table made complex chemistry intuitive. What they share is the power to turn raw data into a clear story that resonates beyond a spreadsheet. Data visualization isn’t only about creating something that looks good; it’s about revealing insights, starting conversations, and inspiring real change. You might be a seasoned data professional or just someone who loves a good chart. I hope these examples encourage you to think carefully about how you present information. Use your visuals to inform, connect, and make a difference.

If you’d like to spend less time on repetitive chart‐building and more time on the insights themselves, consider automating your workflow with the Datylon Report Server. By using Datylon’s API to generate and update charts and reports automatically, you can ensure that your visuals stay up to date without manual effort, so you can focus on what really matters.

Curious about automated reporting? Explore our dedicated blog article or schedule a personalized demo with one of our experts.

Originally published at https://www.datylon.com.

You may agree that environmental, social, and governance (ESG) reporting can feel complicated. You deal with many frameworks, tricky metrics, and growing investor, customer, and employee demands. This makes it hard to know where to begin.

This ultimate guide aims to make ESG reporting clear, practical, and achievable for you. You’ll find out what ESG reporting is. You’ll learn how to pick the right frameworks and metrics for your business. Plus, you’ll see how to visualize your data well. You’ll also explore inspiring examples from companies that do ESG reporting well.

By the end, you’ll feel more confident creating ESG reports that engage your audience and help future-proof your organization.

ESG Reporting Explained: What It Is and How It Works

Remember when a company’s success was measured only by how much money it made? Those days are fading quickly. Today, your investors, customers, and even your employees expect your business to care about more than just profits. They want transparency about your environmental impact, social responsibility, and the decision-making process. This is exactly where ESG reporting comes into play.

What Does ESG Stand For?

ESG stands for Environmental, Social, and Governance. But what do these categories actually include?

Environmental factors relate directly to how your company affects the planet. This can include:

• greenhouse gas emissions
  energy use
• water and resource consumption
• waste management
• pollution control practices.

Social factors look at how your company treats people, both employees and communities. This can include:

• fair labor practices
• diversity and inclusion
• workplace safety
• employee well-being
• involvement in community initiatives

Governance factors are about your company’s internal structures and decision-making processes. This can include:

• transparency
• diversity and independence of your board
• executive compensation
• business ethics
• risk management

What Are ESG Reports?

So how do companies show their progress in these areas? This is where ESG reports come in. An ESG report is a clear summary showing how your company performs in environmental, social, and governance areas. These reports show your company’s actions, goals, and results, making it easy for everyone to see your progress.

Since ESG covers a wide range of topics, companies usually need to decide which ones are most relevant to their business and stakeholders. This process is called a materiality assessment. The results help determine which ESG topics should be prioritized and reported on. Most ESG reports focus on these material topics. However, some companies also include broader ESG commitments or required disclosures.

We’ll explore what to include in your ESG report later in this blog article.

Why Your Business Should Care About ESG Reporting

Still wondering if ESG reporting is worth the effort? Whether you’re a small business or a global brand, ESG reporting can offer real strategic value-not just regulatory compliance. We listed the most important benefits for you:

1. Transparency builds trust. Being open about your environmental, social, and governance efforts creates credibility. It shows stakeholders that you’re serious about accountability and willing to be honest, even when things aren’t perfect yet.
2. It attracts investors. More investors are using ESG performance as a decision-making tool. A solid ESG report shows you plan for the long term. It means you manage risks well and meet market expectations.
3. It enhances your brand reputation. Customers increasingly expect companies to act on their values. ESG reporting helps you show-not just tell-that you’re committed to making a positive impact.
4. It helps you spot risks and opportunities. The process of ESG reporting often reveals blind spots or opportunities for improvement. It can show ways to save money, new ideas, or risks that need your focus.
5. It gives you a competitive edge. People want to work for, buy from, and partner with companies that align with their values. Transparent ESG efforts can help attract and keep employees, loyal customers, and purpose-driven partners.
6. Sometimes, it’s not optional. Regulations are increasing. Depending on your region or sector, ESG reporting might already be a legal requirement-or it could be soon. See the next chapter on ESG frameworks for more on this topic.

Whether you’re doing it to meet regulations or to strengthen your business, ESG reporting is no longer just a “nice-to-have.” It’s becoming a core part of how companies operate and communicate.

But where do you start? That depends on whether you need to follow mandatory rules-or if you’re choosing to report voluntarily.

Finding Your Way: Voluntary vs. Mandatory ESG Frameworks

Before you dive into creating your ESG report, it’s important to understand which reporting frameworks apply to your business. Are you legally required to report on ESG topics? Or are you doing it voluntarily to meet stakeholder expectations and stand out in your market?

To understand better, let’s look at the two main types of ESG frameworks:

• Mandatory: These are required by law.
• Voluntary: These are used to follow best practices or meet stakeholder needs.

Below is a quick overview of the most well-known ones, grouped by type. Each one links to its official source so you can explore further. After that, we will provide a checklist for you to check whether ESG reporting is mandatory for you.

Types of ESG Frameworks (And Where to Learn More)

Mandatory Frameworks (Regulatory Compliance)

• CSRD (Corporate Sustainability Reporting Directive)
  Applies to large EU companies and requires reporting on a wide range of ESG topics using the European Sustainability Reporting Standards (ESRS).
• SFDR (Sustainable Finance Disclosure Regulation)
  Applies to financial market participants in the EU and requires disclosure of sustainability risks and impacts in financial products. While not a direct ESG reporting standard for corporates, it influences what data financial actors request from companies.
• TCFD (Task Force on Climate-related Financial Disclosures)
  Focuses on climate-related financial risks. Mandatory in several countries, including the UK and Japan, and supported globally. Often used as a foundation for climate-related disclosures.
• SEC Climate Disclosure (Upcoming)
  A proposed U.S. rule that will require public companies to disclose climate-related risks and greenhouse gas emissions.

Voluntary Frameworks (Best Practices)

• GRI (Global Reporting Initiative)
  A widely used framework that helps companies report on a broad range of ESG topics, including social and environmental impact.
• SASB (Sustainability Accounting Standards Board)
  Offers industry-specific standards focusing on ESG issues that are financially material to investors.
• CDP (Carbon Disclosure Project)
  Helps companies disclose environmental data, such as emissions and water usage. Frequently used to respond to investor requests and support climate transparency.
• ISSB (International Sustainability Standards Board)
  A new global initiative aiming to unify ESG disclosure standards, building on TCFD and SASB.

Do You Need to Report ESG Data?

Disclaimer: This section provides general guidance and should not be considered legal advice. ESG reporting requirements can vary depending on your specific circumstances and location. Always consult local regulations or legal experts to confirm your reporting obligations.

Not every organization is required to publish ESG reports yet-but that’s changing quickly. In the coming years, sustainability disclosures will become mandatory for more companies, especially in major markets like the EU, UK, and the US. Here are some key questions to help you assess whether your company will need to comply:

✅ Are you a large company based in the EU?
(Defined as having two out of the following criteria: more than 250 employees, €40 million in turnover, or €20 million in total assets.)

→ Yes? Then the CSRD (Corporate Sustainability Reporting Directive) applies. Reporting will be phased in starting from 2025 for FY2024 data, depending on the size and type of your company. Learn more at EFRAG .

✅ Are you a publicly listed company?
(Listed on exchanges in the EU, US, UK, China, or other jurisdictions with ESG regulations.)

→ Yes? Then you’re likely already facing or soon to be subject to mandatory ESG disclosures under local rules, such as CSRD in the EU or upcoming SEC regulations in the US.

✅ Are you a financial institution, insurer, or investment firm operating in the EU?

→ Yes? Then the SFDR (Sustainable Finance Disclosure Regulation) applies. You must disclose how sustainability risks are integrated into investment decisions.

✅ Do you operate in a country with ESG reporting laws?
Regulations vary significantly by region, and new rules are being introduced regularly. To check what applies to your country, here are a few helpful resources:

• EFRAG — for EU sustainability reporting guidance under CSRD and ESRS
• UK Financial Conduct Authority — for TCFD-aligned climate disclosures in the UK
• SEC (U.S. Securities and Exchange Commission) — for updates on U.S. climate disclosure rules
• Japan FSA — for ESG rules on listed companies in Japan
• China Securities Regulatory Commission (CSRC) — for ESG disclosure rules in China (in Chinese). If you work in these areas, ESG reporting may be required. Be sure to consult local regulatory websites or legal advisors.

→ If you operate in one of these or similar jurisdictions, ESG reporting might be mandatory. Be sure to consult local regulatory websites or legal advisors.

✅ Are your investors, partners, or customers requesting ESG information?
→ Even if not legally required, ESG reporting may be expected. Voluntary frameworks like GRI or CDP can help you meet those expectations and prepare for future regulation.

If you answered “yes” to any of the above, it’s time to look into the reporting standards and frameworks that apply to you. If not, you can still gain value from voluntary ESG reporting-and get ahead of growing expectations.

How Do Companies Publish ESG Reports?

Once you know if ESG reporting is voluntary or mandatory and which ESG frameworks apply to your company, the next step is choosing how to report. You can share ESG information in various formats. You can decide what suits your business and stakeholders best.

Here are some common approaches:

Dedicated ESG or Sustainability Report
A standalone report focused entirely on ESG topics. These reports often align with frameworks like GRI , SASB , or CDP and are ideal if you want to show depth and commitment.

A well-known example is Apple’s Environmental Progress Report 2024 . While it’s not a full ESG report but rather an environmental sustainability report, there’s still a lot to learn from it.

Apple’s report is a great showcase of how strong design and clear storytelling can bring sustainability efforts to life. The layout is clean and polished, fully in line with their branding. A simple timeline highlights key milestones, making their progress easy to follow.

What really stands out is the use of data visualizations. The charts are easy to read and often include annotations that add helpful context. This makes the data feel more accessible and meaningful.

Annual Report with ESG Section(s)
A company’s annual report that has a section(s) focused on ESG or sustainability. These sections can differ in detail. Some offer a broad overview, but Ørsted’s Annual Report 2024 is detailed and well-integrated. Annual reports are generally finance-focused and aimed at investors, with ESG reporting supporting that financial story.

Ørsted’s 2024 Annual Report is a great example of how to integrate ESG and financial reporting into one clear and cohesive document. The structure follows best practices, including an extended double materiality assessment. Each of the three ESG pillars is given proper attention, supported by clear KPIs, year-on-year comparisons, and well-explained targets.

What really makes the report stand out is its use of strong, thoughtful data visualizations. Many charts not only show past performance but also include future targets, which helps readers easily understand progress. Overall, the report feels open and well-organized, with a clear connection between strategy and sustainability goals.

Integrated Report
A strategic report that blends financial and ESG data. It shows how the company builds long-term value. ESG factors are not separate-they’re embedded throughout the report. Integrated reports typically follow frameworks like the Integrated Reporting Framework (IIRC) or the ISSB standards. They are also more holistic. They often target a wide audience. This includes shareholders, investors, employees, policymakers, and the general public. An example is the 2024 SAP Integrated Report .

Website Disclosures or ESG MicrositesAn easy way for companies to share their ESG data is through their website or a special page called an ESG microsite. A microsite is a small website or a separate section of the main website that focuses on one topic-in this case, ESG reporting. These pages often include things like interactive charts, downloadable PDFs, and clear information that’s easy to explore.

For example, besides its full annual report, SAP also has a special CSRD webpage . It shows important ESG data using interactive visuals and clear explanations across several pages.

Regulatory SubmissionsIn some places, you must submit ESG data officially. This includes CSRD in the EU and TCFD -based reports in the UK.

​​Choosing the right reporting format depends on your goals, audience, and regulatory obligations. No matter if you want to meet compliance needs or share a great sustainability story, there’s a format for you.

Next up, we’ll look at what actually goes inside an ESG report-so you know what to include once you’ve chosen your approach.

General Structure of an ESG Report

An effective ESG report communicates your company’s sustainability story clearly and transparently. But what exactly goes into an ESG report? Although each business is unique, there are common metrics and KPIs you’ll generally find across most ESG reports.

Starting with a Materiality Assessment

Start your ESG reporting journey with a materiality assessment. This strategic process helps you find and prioritize the ESG topics that matter most to your business and stakeholders.

This assessment includes feedback from internal stakeholders, like employees and leaders. But it also collects feedback from outside groups like investors, customers, and community members. The process usually involves surveys, workshops, or interviews. This helps grasp the importance of different ESG topics from various viewpoints.

Your materiality assessment results shape your ESG report. This helps you focus on the most important and relevant issues. To learn more about conducting a detailed materiality assessment, you can refer to resources such as this guide by GRI .

Clearly State Your Frameworks

At the beginning of your report, clearly mention which frameworks or standards you’re following, such as GRI, SASB, TCFD, CSRD, ISSB, or CDP. Explicitly stating these helps stakeholders understand the foundation of your reporting.

Typical Flow of an ESG Report

A well-structured ESG report typically includes the following:

Begin with:

• A visually engaging Front Page highlighting your company’s sustainability vision.
• A clear Table of Contents for easy navigation.
• A strong CEO or Leadership Statement to show your company’s dedication to ESG. It offers strategic insights and highlights your commitment to ESG issues.
• An Executive Summary or Highlights section. This page shows key ESG achievements and challenges from the reporting period.

The main body should:

• Divide the content into three sections: Environmental, Social, and Governance. Each section should include information backed by data in the form of charts, tables, or infographics. You can also include relevant case studies or examples to show performance.
• Clearly state how the ESG activities in each section match your selected frameworks and standards.

Conclude with:

• A detailed Data and Methodology section or appendix should explain the methods used. It must include definitions and calculations. Also, provide clear ESG data for transparency and verification.
• You can add more details in the appendices. They may include case studies, summaries of stakeholder engagement, methodology details, or regulatory disclosures.

General vs. Framework-specific Requirements

The structure above represents common reporting practices. However, each ESG framework may have specific additional requirements:

• GRI: Broad environmental and social disclosures (GRI Standards).
• SASB: Financially material industry-specific disclosures (SASB website).
• TCFD: Climate-related financial disclosures and scenario analysis (TCFD recommendations).
• CSRD: Detailed European Sustainability Reporting Standards emphasizing double materiality (European Commission website).

To learn about visualizing Double Materiality, check out our article series on the subject.

• ISSB: Investor-focused ESG integration (IFRS website).
• CDP: Detailed disclosures on climate, water, and forestry (CDP disclosure resources).

Audit-Ready and Digital Tagging Requirements

If you’re following mandatory frameworks like CSRD, your ESG report must be audit-proof. Auditors need to easily verify your data, sources, and claims. This requires clear, structured tagging of ESG information. Each disclosure should be explicitly linked to the relevant standards or frameworks.

Under CSRD, your ESG disclosures must also be digitally tagged using XBRL (eXtensible Business Reporting Language). This standardizes your data and makes it machine-readable. Moreover, it facilitates verification by auditors. Lastly it enables stakeholders to easily compare ESG data across different companies.

Ensure each ESG disclosure includes references to:

• Specific CSRD/ESRS reporting requirements
• Exact data sources
• Calculation methodologies
• Verification status and processes
• Dates and version control for documentation

Proper audit-proofing practices enhance your report’s transparency, accuracy, and usability. This significantly saves time during audits and improves stakeholder trust ( Apiday ).

Now that you’ve structured your ESG report effectively, the next step is zooming in on ESG scores that are shown in the body of the ESG Report. These scores help stakeholders quickly assess your company’s ESG performance.

ESG Scores: Understanding the Numbers Behind the Story

Now that we’ve discussed the overall structure of an ESG report, let’s focus on a key part: ESG scores. These scores distill complex sustainability efforts into simplified, comparable numbers. But where do they come from, what do they mean, and how should you interpret them?

Who’s Behind ESG Ratings?

ESG scores usually come from third-party rating agencies, research firms, and data providers. Some of the most well-known include MSCI , Sustainalytics , S&P Globa l, and Bloomberg . These organizations evaluate companies based on publicly available information and their proprietary methodologies. Some investors and big customers also create their own scoring models to evaluate ESG performance.

Common Types of ESG Scores

There is currently no universal standard for how ESG results should be scored or presented. Companies have the flexibility to choose how they want to communicate their ESG performance. Scoring agencies each use their own methodology. As a result, the same company might receive different scores from different providers, even based on the same data.

Despite this lack of standardization, some scoring formats are more commonly used than others:

• Single-factor scores look at individual ESG metrics. Examples of areas are carbon footprint, board diversity, or energy consumption. They offer targeted insights into specific performance areas.
• Composite scores roll up multiple ESG metrics into a single score. They give a high-level overview of a company’s overall sustainability performance.
• Letter grades (like A to F) are often used to reflect ESG risk levels or maturity. Higher grades generally suggest stronger performance or lower exposure to ESG risks.
• Risk/opportunity ratings look at how exposed a company is to ESG risks. And it shows how well the company handles or takes advantage of those risks and opportunities.

It’s important to note: a higher score doesn’t always mean a better score. In some cases, a higher score may reflect a higher exposure to ESG topics and not necessarily better management. That’s why clarity and transparency are essential when sharing these numbers.

Providing Context

ESG scores are more meaningful when paired with relevant context. Without it, numbers alone can be misleading or too abstract. Readers need a frame of reference to understand what a score truly represents and how it fits into the bigger picture.

You can do this by referencing to:

• Internal sustainability goals: It can show long-term targets like net-zero emissions by 2030 or 2050. These internal benchmarks help show progress over time and commitment to long-term change.
• Industry averages: It can show how your company stacks up against peers in the same sector.
• External benchmarks: can show ESG indexes or third-party rankings. You can do this to show credibility and consistency.
• Previous reporting year: It shows comparisons with previous reporting years to highlight progress.

Showing trends over time, rather than one-off scores, helps make your ESG journey more transparent. Context helps transform scores into a story. It highlights not just where the company stands today, but where it’s headed and how it plans to improve.

Why ESG Scores Matter

ESG scores make your data digestible. They give readers a way to quickly assess how your company performs across key sustainability topics. But scores are only as valuable as the context you provide. Clearly explaining the source, methodology, and meaning behind each score makes your report more informative. Besides that, it will make your ESG Report trustworthy and actionable.

In the next chapter, we’ll explore which types of data visualizations work best for presenting ESG scores and other results. This will make your report not only clear, but also visually compelling.

Best Data Visualizations for ESG Reporting

Numbers tell the story, but visuals bring it to life. Now that we’ve explored the role of ESG scores, let’s dive into the best ways to visualize ESG performance. Effective data visualization can help clarify complex results. They can highlight key takeaways and build trust with your audience.

In this chapter, we’ll cover the most commonly used charts in ESG reporting. Moreover, we will discuss some specific topics in ESG Reporting. We will show how to visualize materiality, double materiality, and long-term sustainability targets.

Commonly Used Charts in ESG Reporting

Some charts are timeless for a reason-they communicate clearly and efficiently. Here are a few of the most popular and effective chart types for ESG reports:

• Bar charts: Ideal for comparing ESG performance across categories or over time (e.g., energy consumption per year).
• Line charts: Great for showing trends and changes over time. Examples are emissions reductions or improvements in safety metrics.
• Scatter plots: Frequently used to visualize both materiality assessments and double materiality. See the dedicated sections below for more detail on how to use them effectively.
• Pie or donut charts: Useful for illustrating compositions. For example workforce diversity by gender or age group.
• Dot plots: Allow quick comparisons across years or departments, often paired with targets.
• Bullet charts: Compact visuals that show actual performance, targets, and qualitative performance ranges in one view.
• Gauge charts: Intuitive visuals that indicate progress toward a single ESG metric (like a speedometer).
• Heatmaps: Best for showing relationships between variables or concentrations of activity, such as ESG risks across regions.
• Sunburst charts: Effective for hierarchical data, like ESG scores by topic.

Each chart type has strengths and limitations. When choosing a visualization, consider what message you want to convey and who your audience is.

Visualizing Materiality

Materiality assessments identify which ESG topics matter most to your business and stakeholders. Tables and lists are often used to present material topics. However, visualizations can make your results easier to grasp and more actionable.

The most common chart type for visualizing materiality is a scatter plot. Each topic is plotted along two axes. Typically one for relevance to stakeholders and the other for impact on the business. This allows you to clearly show which topics are considered highly material from both perspectives.

Visualizing Double Materiality

Double materiality considers two dimensions:

• Financial materiality: impact on the company
• Impact materiality: impact of the company on society or the environment.

It’s a core principle in CSRD and other emerging regulations. There are many similarities between visualizing materiality assessments and visualizing double materiality. Both use two-dimensional space to map topics and make priorities visually intuitive.

Common double materiality charts include:

• Scatter plots: Plot topics by financial vs. impact materiality. Simple and flexible.
• Matrix charts: Divide the scatter plot into zones (e.g., high/medium/low), making it easier to interpret.
• Bubble charts: Add a third dimension (e.g., stakeholder importance) through bubble size.
• Scatter plots with zones: Overlay the scatter with organic or linear background zones to show thresholds or levels.

Want a deep dive into this topic? Check out our full blog series on Visualizing Double Materiality.

Visualizing Long-Term ESG Targets

With frameworks like CSRD, companies must report on both current performance and long-term sustainability goals. That’s where visualizing progress toward targets becomes critical.

Depending on your reporting needs, the following examples of chart types work well:

• Bar charts with extra bars: Show past data alongside future targets.
• Bar charts with target lines: Add a horizontal line to benchmark performance.
• Bullet charts: Show actual vs. target plus performance zones (e.g., poor, average, good).
• Dot plots: Compact and clear for tracking progress across many goals.
• Line charts: Show trends over time and how close the organization is to reaching its goals.
• Stacked area charts or waterfall charts: Useful for showing how different initiatives contribute to a long-term goal.

For more detailed guidance and visual examples, read our article on Visualizing Long-Term ESG Targets.

Wrapping Up

Great data visualization helps bring your ESG story to life. Whether you’re showing performance or future goals, the right chart makes complex information easier to understand and more meaningful to your readers.

Now that you have a better idea of how to visualize your ESG data, it’s time to put everything into practice. In the next chapter, we’ll walk you through a step-by-step guide for creating an ESG report.

So far, we’ve covered why ESG reporting matters, what frameworks exist, and what typically goes into a report. Now it’s time to bring it all together. This step-by-step guide walks you through the process of creating your ESG report.

Creating Your ESG Report: A Practical Step-by-Step Guide

So far, we’ve covered why ESG reporting matters, what frameworks exist, and what typically goes into a report. Now it’s time to bring it all together. This step-by-step guide walks you through the process of creating your ESG report.

Step 1: Find Out If ESG Reporting Is Required

Before you begin, check if ESG reporting is mandatory for your company. In the chapter Finding Your Way: Voluntary vs. Mandatory ESG Frameworks, we explained how to assess this based on your size, location, industry, and stakeholder demands. If you’re unsure, consult local legal experts or use official resources like EFRAG, the SEC, or CDP.

Step 2: Choose the Right Framework(s)

Based on your situation, choose the reporting framework that fits best. Some companies need to follow CSRD or TCFD. Others may choose GRI or SASB voluntarily. You can also combine multiple frameworks to meet both legal and stakeholder expectations. Clearly state your chosen frameworks early in your report-this sets the stage for everything else. We covered these frameworks earlier in the chapter Finding Your Way: Voluntary vs. Mandatory ESG Frameworks.

Step 3: Identify Material Topics

A strong ESG report focuses on what matters most. This is where a materiality assessment comes in. You’ll gather feedback from both internal and external stakeholders. This includes employees, investors, customers, and local communities. Use this feedback to identify which ESG topics are most relevant.

Then, check if your chosen frameworks require additional topics to be reported. If you’re working with CSRD for example, remember that you also need to assess double materiality.

Step 4: Collect and Organize Your Data

Now it’s time to get practical. Collect data for each of your material topics-whether it’s emissions, diversity metrics, or governance policies. This might involve combining data from HR, operations, finance, and external partners.

Make sure to also document how you collected, calculated, and verified your data. This will go in the methodology section of your report.

If your ESG reporting falls under mandatory frameworks like CSRD, your data also needs to be audit-proof . To do this, clearly tag your data digitally using XBRL. XBRL tags link each ESG disclosure directly to the related reporting requirements, sources, calculation methods, verification steps, and dates. Clear tagging makes it easier for auditors to review your data and helps stakeholders trust the accuracy of your report.

Step 5: Choose the Format of Your Report

Decide how you want to publish your ESG report. Examples of options are a dedicated sustainability report, an integrated report, a section in your annual report or a web-based ESG page or microsite.

Each format has pros and cons, depending on your audience. We covered these formats earlier in the chapter How Do Companies Publish ESG Reports.

Step 6: Report the Results

This is where it all comes together. Start writing and designing your report using a mix of:

• Text: context, goals, and narratives
• Charts and data visualizations: to clearly show performance
• Illustrations and icons: to guide the reader
• Case studies: to add real-world examples

Make sure your structure follows the flow we outlined in the chapter General Structure of an ESG Report. This includes a clear intro, ESG sections, and a transparent appendix or methodology page. For tips on choosing the best data visualizations, refer back to our chapter Best Data Visualizations for ESG Reporting.

Step 7: Share and Communicate Your ESG Report

Promote your ESG report across relevant channels. This can include your website, newsletters, social media, investor meetings, and employee updates. Adjust the message so it fits the different groups you are sharing it with. Each reader, from analysts to customers, values different aspects of your ESG journey. Consider summarizing your report with infographics, slide decks, or interactive dashboards. This makes the key messages easy to understand and share.

Step 8: Analyze and Act on the Results

Once your report is published and shared, it’s time to reflect on the findings. This step is all about turning insights into meaningful actions. Analyze your performance across ESG topics and use the results to:

• Set or refine internal ESG targets
• Inform your risk management and strategy
• Update relevant policies or programs
• Launch new initiatives to close gaps in performance

Your ESG report is more than a communications tool-it’s a guide for action and continuous improvement.

Step 9: Evaluate the Reporting Process and Plan for Next Year

While Step 8 focuses on analyzing your sustainability results, this step looks inward at your reporting process. Evaluate how the process went: What worked well? Where did you face challenges? What can be done better next year? Use this internal feedback to:

• Improve collaboration between departments
• Refine your materiality assessment approach
• Strengthen your data collection and review workflows
• Enhance the clarity or layout of your report

Think of this step as the closing loop. This step ensures each year’s experience leads to a more improved, accurate, and engaging ESG report in the future.

Wrapping Up

And there you have it-the full process from start to finish. Creating an ESG report takes time and teamwork. With the right steps in place, it becomes a powerful tool for strategy, transparency, and growth.

In the next chapters, we’ll zoom out again. You’ll learn how different readers use your ESG report, explore challenges and discuss best practices. Moreover, we will show some inspiring real-world examples to improve your next reporting cycle.

How Readers Actually Use Your ESG Reports

Your ESG report isn’t just a formality-it’s a powerful tool for sharing your sustainability journey with your readers. Each reader group interacts with your ESG report in its own way. This shapes their decisions, perceptions, and how they connect with your company.

Investors
Investors closely analyze ESG reports to evaluate risks and opportunities related to sustainability. They want clear ESG performance metrics. This helps them assess long-term viability and make investment decisions. Clear ESG data helps investors see how well your company handles environmental, social, and governance risks.

Customers
Customers check ESG reports to guide their buying choices. They prefer brands that match their values and show real commitment to sustainability. Research by McKinsey and NielsenIQ shows that products with sustainability claims grew faster than those without. This suggests that consumers are more likely to support brands that clearly share their sustainability efforts. So good reporting can help build brand loyalty and draw in eco-friendly and socially aware consumers.

Employees
Employees look at ESG reports to understand their company’s values, policies, and social responsibility. Clear ESG reporting lifts employee morale, draws in top talent, and unites everyone on sustainability goals.

Research by Deloitte shows that companies with a sustainability program have more satisfied employees, with 79% planning to stay in their jobs, compared to just 40% at companies without sustainability programs. Additionally, employees at sustainability-focused companies feel more inspired and experience a greater sense of accomplishment in their work.

Community members
Community members and NGOs use ESG reports to keep companies accountable for their sustainability efforts. They evaluate how your operations affect local communities and ecosystems. They depend on clear and reliable reports to advocate well. PwC’s ESG Toolkit for NGOs shows that NGOs want clear transparency in ESG reports. This includes not only community efforts but also details on internal operations, governance, and environmental effects. This clarity helps NGOs partner effectively and ensures alignment on sustainability goals.

Wrapping Up

When you know how different readers use your ESG report, you can make your sustainability messages more focused, meaningful, and effective.

In the next chapter, we’ll look at common challenges in ESG reporting. We’ll also share practical best practices to improve your reporting process.

Common ESG Reporting Challenges (and Practical Tips to Solve Them)

Let’s be honest-ESG reporting isn’t always easy. Even with the best intentions, many companies struggle with the process. Whether it’s figuring out what to report or collecting the right data. Or just getting started without fear of doing it wrong, there are plenty of hurdles along the way.

The good news? You’re not alone. These challenges are common and most of them can be managed with a bit of structure, transparency, and teamwork. Below, we walk you through the most frequent ESG reporting challenges. We will also offer practical tips to help you move forward with confidence.

1. Uncertainty and Fear of Doing It Wrong

One of the most common blockers is simply not knowing where to start-or being afraid to make mistakes. With so many expectations, regulations, and the risk of being called out for greenwashing, some companies hesitate or keep things too vague just to play it safe.

Tip: Be honest about where you are in the process. Nobody expects perfection. Clear communication about progress and pain points builds trust — and helps others see your commitment is real.

2. Too Many Frameworks, Too Little Clarity

GRI, CSRD, TCFD, ISSB, SASB… it’s easy to get lost in the ESG alphabet soup. Many businesses struggle to understand which frameworks apply, how to combine them, or what’s required by law versus what’s just a best practice.

Tip: Start by figuring out if ESG reporting is mandatory for your company (check CSRD or SEC rules, for example). Then choose frameworks based on what your stakeholders care about. Look for overlaps so you don’t end up duplicating work.

3. Scattered Data and No One Really in Charge

ESG data often lives in different departments-like HR, finance, and operations. And sometimes, no one really feels responsible for pulling it all together. That makes it hard to find the right data, keep it consistent, or even know where to start.

Tip: Make it clear who’s doing what. Assign one person or a small team to lead the reporting process, and ask each department to take ownership of their part. Use a shared file or system so everything stays in one place. This makes collecting data easier — and helps the whole process run more smoothly.

4. Difficulty Measuring Qualitative or Long-Term Topics

Some topics-like social impact, diversity, or future climate goals-are harder to measure than others. It can feel tricky to report on them when the data isn’t perfect, or when the impact will only become visible over time.

Tip: Don’t wait for perfect numbers. Use the data you have, explain how you’re collecting it, and be clear about what you’re still working on. Combine this with storytelling or real-life examples to show progress in a relatable way.

5. Fear of Showing Weaknesses

It’s tempting to only share what’s going well. But reporting only on the good stuff can raise doubts or come across as selective. Today’s readers-and regulators-expect honesty.

Tip: Show both your strengths and the areas where you’re still improving. This shows maturity and builds trust. Even small steps in the right direction are worth sharing when they’re framed with honesty and intention.

Wrapping Up

There’s no one-size-fits-all approach to ESG reporting-and that’s okay. What matters most is that you start, stay transparent, and keep improving. The challenges we’ve covered here are normal. Every company faces them in some form.

The key is not to let complexity or fear get in the way. With a solid process, the right people involved, and a mindset of progress over perfection, your ESG reporting can become more than a requirement. Your ESG Report can become a valuable tool for strategy, communication, and change.

Looking Ahead: What’s Next for ESG Reporting?

Sustainability reporting is evolving fast. What started as a voluntary effort by some forward-thinking companies is now becoming a legal requirement for many. But as ESG grows, so do the expectations-and the tensions.

1. ESG Is Becoming Politicized (Especially in the U.S.)

What this means:

In the U.S., ESG has become a hot political topic . Some states and politicians are pushing back, calling ESG initiatives “woke capitalism.” We’re already seeing moves to limit ESG-based investing. Also, some major companies scale back diversity, equity, and inclusion (DEI) programs.

• ESG still matters to many investors-but some companies are getting quieter about it.
• Greenhushing (not talking about your sustainability efforts to avoid backlash) is on the rise.
• U.S. and EU approaches to ESG may continue to drift apart.

2. Europe Is Doubling Down on ESG

While ESG sparks debate in the U.S., the EU is moving full steam ahead. Through the Corporate Sustainability Reporting Directive (CSRD) and European Sustainability Reporting Standards (ESRS), Europe is setting strict requirements for what and how companies must report.

This isn’t just about checking boxes. The EU is working to make ESG reporting more structured, standardized, and embedded in everyday business. And it’s happening fast:

• 2024: CSRD applies to large companies already covered under the older NFRD rules.
• 2025: Other large EU companies must start collecting ESG data in 2025 for reports due in 2026.
• 2026–2028: The scope expands to include listed SMEs and certain non-EU companies doing significant business in the EU.

What this means:

• If you work with or within Europe, ESG reporting is no longer optional for a growing number of companies.
• Even global companies based outside the EU need to align with these expectations.
• These changes are raising the bar for ESG worldwide-especially across global supply chains.

3. Increased Scrutiny Means More Greenwashing Risks

With more regulation comes more attention. Investors, NGOs, journalists, and regulators are watching your ESG report — and they’re not just skimming.

What this means:

• Don’t just write what sounds good-prove it.
• Make sure your data is accurate, verified, and ready to be audited.
• Expect more demand for assurance tools and transparent reporting methods.

4. Technology and AI Are Reshaping Reporting

While the political debate plays out, ESG tech is charging forward. AI, automation, and new platforms are making it easier to collect, manage, and report ESG data.

What this means:

• If you start using these tools now, you’ll save time-and gain insights-later.
• Watch for more real-time dashboards, automated reporting, and smarter analytics tools.

Curious how Datylon can support your ESG reporting? Get in touch for a live demo.

5. The Rise of Social and Governance Reporting

Environmental issues used to steal the spotlight, but that’s changing. More companies are stepping up their reporting on social and governance topics. According to KPMG , reporting on social risks jumped from 49% to 74% between 2022 and 2024, and governance from 44% to 77%.

This shift is backed by new findings from S-RM . They show that companies and investors are beginning to pay closer attention to human rights, equality, diversity and inclusion (EDI), and responsible supply chains. These topics are climbing the priority list. They are driven by regulation, stakeholder expectations, and growing public awareness.

Yet many companies still feel unprepared. In S-RM’s survey, 74% said they don’t believe their ESG programs are fully mature. This may help explain why many reports are still labelled as “sustainability reports” or “environmental reports” and focus mainly on the environmental side-leaving out social and governance issues entirely. But ESG is about the full picture, not just the climate.

What this means:

• You can’t ignore areas like employee well-being, board diversity, and ethical supply chains.
• These topics are no longer “nice to have”-they’re essential.
• Covering all ESG pillars helps build trust, stay ahead of regulations, and avoid blind spots.

Wrapping Up

Despite the politics and complexity, ESG isn’t going anywhere. It’s changing, and fast. Companies that stay open, transparent, and ready to adapt will be in the best position to succeed.

Whether you’re facing regulations or just want to build a more sustainable business, now’s the time to strengthen your ESG reporting game.

Concluding Thoughts

Thanks for sticking with us through this ultimate guide to ESG reporting. We know-it’s a lot. From figuring out frameworks and collecting the right data to making sure your report is audit-ready and engaging to read, ESG reporting can feel like a challenge. But hopefully, it now feels more doable too.

Whether you’re just getting started or looking to improve your next reporting cycle, the most important thing is to approach ESG reporting with honesty, clarity, and a mindset of continuous improvement. You don’t have to be perfect. Just start where you are, be transparent about the process, and keep learning.

And if you’re ever feeling stuck-come back to this guide, check out the examples, or get in touch. Because the more companies commit to meaningful ESG reporting, the more positive change we’ll see-for businesses, people, and the planet.

👉 Want expert support in designing and publishing your ESG report? Discover our ESG Report Publishing services.

Glossary

ESG — Stands for Environmental, Social, and Governance. These are three key areas companies report on to show their impact beyond just financial performance.

CSRD — Corporate Sustainability Reporting Directive. An EU regulation requiring large companies to report detailed ESG information using specific standards (called ESRS).

ESRS — European Sustainability Reporting Standards. A set of detailed reporting standards that companies under CSRD must follow.

Materiality Assessment — A process to identify which ESG topics are most important to your business and its stakeholders. It helps decide what to include in your report.

Double Materiality — A concept in CSRD that looks at two angles: how ESG topics affect your company, and how your company impacts society and the environment.

XBRL — Short for eXtensible Business Reporting Language. A digital tagging system that makes ESG data machine-readable, so it’s easier to audit, compare, and analyze.

Greenwashing — Making your company look more sustainable than it really is-by exaggerating, omitting details, or presenting misleading information.

Greenwishing — When a company makes ambitious sustainability claims or goals without a clear plan to achieve them. The intention is good, but the actions don’t match.

Greenhushing — Choosing not to publicly talk about your sustainability efforts-even when they’re meaningful-out of fear of criticism or backlash.

Framework — A structured set of guidelines or standards (like GRI, CSRD, or TCFD) that helps companies decide what ESG topics to report on, and how to report them.

GRI / SASB / TCFD / ISSB / CDP — Popular ESG reporting frameworks. Some are mandatory in specific regions, others are voluntary and used to meet investor or stakeholder expectations.

Sustainability Report — A general term often used for ESG reports that focus mostly on environmental performance. Some companies publish these without fully covering the “S” and “G” aspects of ESG.

KPIs (Key Performance Indicators) — Specific metrics used to measure performance on ESG topics, like carbon emissions, board diversity, or employee turnover.

Integrated Report — A report that combines both financial and ESG information into one document, showing how a company creates value over the short and long term.

Assurance — A formal review (usually by a third party) to verify the accuracy and reliability of ESG data in your report. Often required for regulatory compliance.

Stakeholders — The people or groups affected by your business or who have an interest in it-like employees, customers, investors, regulators, and communities.

Resources/Further Readings

• What is Environmental, Social, and Governance (ESG)? | IBM
• ESG Watch: New SEC rules under Trump turn up the heat for sustainable investors
• Greenwashing Examples for 2024 & 2025 | Products & Brands
• ESG and Sustainability Insights: 10 Things That Should Be Top of Mind in 2025
• 5 ESG Trends in 2025 - Carbon Trail

https://sites.lsa.umich.edu/mje/2025/01/10/the-future-of-esg-under-the-trump-administration/

https://www.sciencedirect.com/science/article/pii/S2666791624000253

https://www.positiongreen.com/insights/articles/how-esg-reporting-enhances-employee-loyalty-and-trust/

https://sustainabilitymag.com/articles/how-is-woke-capitalism-row-impacting-esg-at-major-firms

Originally published at https://www.datylon.com.