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Log into your Visme dashboard and click Create to get started. Choose a template for any type of project, whether it’s a presentation, an infographic or another design. Many of our templates are premade with chart templates right inside so you can customize them with your own data. You can easily add any chart types to any template by choosing a chart from the Data tab.
Data visualization is to use charts to represent data information. The information it conveys includes the data sources you get and the results of your analysis, and then strengthens the user's understanding and memory through graphics. It is the ultimate goal of our visualization to enable users to obtain more information concisely and clearly.
For example, if the same set of data is presented in the form of a table, it is difficult to gain insight; if the data of each regional dimension is aggregated and presented in the form of a column chart, it is easy to see the differences in data between regions, and insight into the law
Regarding how to design a good data visualization chart, here are three steps: choose a suitable chart, strengthen the visual hierarchy, and adapt the chart responsively.
There are many types of charts for data visualization. When we really start drawing, we often encounter a dilemma: there are so many types, how to choose the right chart? The first basis is to consider the intention of the information to be conveyed, that is, what is the task of the chart to be made, and then choose the expression method by analyzing the relationship between the data; the second level of intention is the content conveyed by the chart. highlight and reinforce.
According to the way of data analysis, each chart corresponds to a data relationship. Clarify the presentation structure from the dimension of the data, and then make a choice based on the data relationship. Those who understand charts may know that the data relationships in general charts include composition, comparison, and distribution. Taking business data as an example, there are common circulation relationships.
The chart of the composition relationship expresses the relationship between the part and the whole, and is used to analyze the proportion of the whole and each part. The general part of the composition relationship is added up to the total. If you just want to compare the size of individual components, you can also use a chart comparing relationships.
Commonly used charts: pie/ring charts, stacked charts, area charts, etc. If hierarchical structures are involved, special structural charts such as rectangular tree charts or sunburst charts will also be used.
Keywords: "proportion, proportion, percentage"
Comparative relationships are a chart type commonly used in fundamental analysis. Within a certain range of values, through the analysis of two or more indicators, changes and gaps can be seen intuitively. Comparative analysis includes two forms: trend comparison and category comparison. Trend comparison is used to represent data changes over a period of time, and category comparison is used to compare data scale.
Commonly used charts: Commonly used charts for trend comparison include line charts, scatter charts, etc.; classification comparisons commonly use bar charts, histograms, and bubble charts, etc.
Keywords: "increase and decrease, rise and fall, rise and fall, fluctuation"
Use spatial partitions to show the distribution relationship between data, which is often used to reflect the correlation of two or more data.
Commonly used charts: scatter plots, heat maps, radar charts, etc.
Key words: "change with..., correlation between A/B, intersection/union", etc.
The flow relationship is a commonly used relationship for B-side data. It can dynamically reflect the relationship, status, and data flow changes between objects under the relevant path. It shows the relationship between multiple states or objects in terms of area or color depth. Flow volume or flow intensity.
Commonly used charts: relationship diagrams, Sankey diagrams, funnel diagrams, progress diagrams, etc.
Keywords: "process step, retention, conversion, relationship"
After selecting the chart type according to the data relationship and analysis purpose, the second step is to select a more suitable display method according to the data characteristics. From the perspective of data analysis, common data features include: variable features, dimension features, hierarchical features, and process features.
There are usually two types of characteristics to distinguish an indicator, which can be divided into continuous data and discrete data according to whether the variable value is continuous or not. Continuous data usually counts the change trend of a set of data, and discrete data usually counts the change of the quantity under each category.
Continuous data: Refers to the data that can be arbitrarily selected within a certain interval, which is called continuous data, and its value is continuous. For data with time factor variables such as height and weight, a line chart is usually used to reflect the change trend.
Discrete data: Refers to data whose values can only be calculated with natural numbers or integer units. For example, the sales volume of the day, the number of people entering the store, etc. represent the data of the classification type, and the column chart can be used to show the characteristics of the variables.
Multidimensional analysis requires visual representation of multiple variables on the same plane, and you can choose to use the polar coordinate system to display multiple dimensional variables. If you want to compare multiple sets of data, you can use different colors for classification
Select the chart according to the analysis perspective: For data with multi-dimensional variables, we need to clarify the analysis perspective and find the corresponding data mapping. For example, in the case of the group of subject scores of multiple classes, if you need to view the overall quality of a class from a global perspective, it is recommended to use the radar chart; to compare the change distribution of single subject scores, it is recommended to use the stacked chart.
Multi-level data consists of multiple parts to form a whole, also known as tree-structured data. In addition to the structure tree diagram representation, the following two diagram types can also be considered:
Rectangular tree diagram: Highlight the proportion of sub-levels. It was originally used to display the structure and size of files on the computer hard drive. It highlights the proportion of each sub-level node in the form of area, which can help users see the hierarchical structure of data and the relationship between each level.
Example: The image above shows the source structure of market sales. The size of the rectangle depends on the average sales of each country. Different types are distinguished by color tone. The depth of the color represents the subset under the classification, and the area reflects the sales ratio. Compared with the general structure tree chart, its advantage is that it can effectively use the space.
Rising sun chart: highlight the subdivision hierarchy relationship. It is nested by multiple ring diagrams, also known as a radial tree diagram, which can not only express the proportion of parts and the whole like a pie chart, but also express hierarchical relationships like a rectangular tree diagram. It is often used in subdivision analysis methods to disassemble things from large to small. The data of each level is represented by a ring, and the closer to the origin, the higher the level of the ring. A sunburst diagram is most effective at showing how a ring is divided into levels, while a treemap is good for comparing relative sizes.
The flow relationship is an important type to express the data conversion process, which not only includes the sum of data in the statistical sense, but also expresses the path of information flow; among them, Sankey diagram and funnel diagram can both express the change of flow in the path, the difference is that Sankey The graph emphasizes the intensity and direction of traffic, while the funnel graph focuses more on highlighting the conversion rate and effect, and is selected according to different expression purposes.
After choosing a suitable chart, it is also necessary to express the information correctly. The display form is beautiful and at the same time it can clearly reflect the characteristics of the data.
Using charts to correctly express information requires designers to avoid bias while strengthening data characteristics
Trends are easy to perceive: If the values of the line chart are too average, the trend will be flat, and sometimes the deviation cannot be reflected; in scenarios where data trends are emphasized, it is recommended to use dynamic value ranges to keep the fluctuations within a certain range, and the proportion of enlarged fluctuations is more prominent.
More accurate display: The histogram relies on the area of the column to reflect the final value, and the use of dynamic range truncation will lead to incomplete data interpretation; always start the y-axis from 0 to more accurately reflect the value in the histogram.
In addition to optimizing the design composition, the choice of color is also an important information expression element of the chart. Color can affect our perception of data, and the wrong color can lead to misinterpretation of information reading. We can set the intention color palette through different analysis purposes, accurately convey information, and at the same time, we can also achieve the unity of color when selecting subsequent projects.
We have introduced the color selection model of the chart in the previous article. By adjusting the range of the color HSL value, the following three intention color palettes can be obtained:
Qualitative type-classification color palette: used to distinguish different types, also known as disorderly color palette. Good for distinguishing categories with no intrinsic order
Quantitative divergence - Divergent color palette: represented by complementary colors on both sides, with a bright middle value, and then darken in different hues at the ends of the scale. Often used to visualize negative and positive values
Quantitative Sequential - sequential swatches: gradients from light to dark or vice versa. Good for visualizing numbers from low to high.
So how do we use these swatches? The following cases will take you to understand the difference.
Qualitative: Use Hue to Categorize
When there is no sequential difference in the data, it is recommended to use the hue value (H) to distinguish; such as defining countries, industries and other types. If you want your charts to look more professional, here are a few tips for sorting swatches:
Select as few hues as possible: It is not necessary to use the entire color circle when picking colors, and it is more professional to use adjacent colors or complementary colors.
Moderate light and dark color palettes: Some visually impaired people cannot distinguish hues, and mainly rely on the difference between light and dark colors to identify them. Through the adjustment of saturation and darkness, a color palette with alternating light and dark is created.
Quantitative: use dark and light color palettes to emphasize internal order
If subcategories are included under the same category, or the data itself has a ranking attribute, it is recommended to use a continuous color palette to highlight their inherent order and make the chart more readable.
Sequential color palette - select the appropriate interpolation: select different interpolation breakpoints according to the distribution of the data to highlight the differences in the data. For example, in the following cases, different value intervals are set according to statistical concepts. The example on the left uses the average linear difference, which reflects the central tendency of the data; the example on the right uses median interpolation, which can be better reflect the shape of the data distribution.
Divergent color palette - pay attention to the recognition of contrasting colors: try to avoid red and green color matching, if you need to use green, yellowish or bluish green is easier to distinguish, and it is more friendly to the visually impaired.
The data visualized in the B-side chart is generally dynamically displayed on the webpage or mobile terminal. Different from flat display or report, after the basic design is completed, it is necessary to consider the environment of the chart display, and adapt the display effect according to different ends to adapt to various complex situations. The interactive features brought by dynamic display also make data display more possibilities.
1. Layout framework adaptation
When displayed on the web page, sometimes the same chart needs to consider the adaptation methods under different containers. Change the adaptive form of elements according to data correlation, convert or hide unnecessary elements, and keep important graphic elements to adapt to the current space; after elements are hidden, use hover interaction to ensure the display of information, keep the readability of the chart and also Avoid overlapping elements.
For example, in the chart in the case, change and hide the chart elements in different sizes to achieve the effect of adapting to the current space.
To adapt to the mobile terminal, the display method of horizontal extension on the webpage needs to adapt to the display of the vertical space on the mobile terminal. In addition to changing the presentation angle, it is also necessary to consider the size of the mobile phone screen and the adaptability of the chart elements to be compatible with related interactive operations.
Information floating layer: In the chart, the information card element is an important component to convey the information content. The floating display on the web page usually takes up a lot of space; after the chart is adapted to the mobile terminal, the information floating layer is changed to the permanent display above the chart , and follow the sliding changes of the fingers to respond to the value, fully displaying the information and avoiding page shaking.
Coordinate axis labels: Too long coordinate labels will overlap during the adaptation process, and omitting them will also cause incomplete information display. We need to give the specification of the response for different axis types. For example, we can adapt text-like axis labels by omitting, wrapping, rotating, etc. to avoid the lack of information. For continuous data type coordinate axes, we can use sampling, conversion units, etc. to adapt to simplify the space and avoid stacking.
Due to the characteristics of the B-end platform, we cannot predict the amount of data imported by customers, and may encounter problems such as poor chart display and difficult data reading due to excessive data volume. In this case, consider data limit scenarios in advance, and allow users to obtain complete information through interactive form changes, improving understanding and more flexible information display.
Zooming and panning: When the data range is too large, the thumbnail axis component increases the value range, and makes the amount of data display controllable by limiting the display range.
Interactive switching view: If there are too many categories of polylines and cannot be omitted, it is recommended to view them in groups. By default, only the current group polyline is highlighted, and other data are displayed in light colors. You can view information of other groups by switching.
Hover mouse highlight: When there are too many categories and it is difficult to distinguish, you can use the interactive method of hover highlight to highlight the associated data group.
There are also interactive behaviors such as hover zoom, click to drill down, and linked charts to form richer charts. Due to the length of the article, I will not give an in-depth explanation in this article, and I can introduce it independently in the future.
Data visualization plays an important role in the B-side design scenario. Only when designers express the beauty of data more accurately can they convey the value of data to users more intuitively. The key to visualization is to enable business personnel to quickly and directly find important data from complex business data and ensure that users can better receive information.
* The above charts are fictitious data and are only used as a case reference
There are more types of charts and graphs than ever before because there's more data. In fact, the volume of data in 2025 will be almost double the data we create, capture, copy, and consume today.
This makes data visualization essential for businesses. Different types of graphs and charts can help you:
Data visualization builds trust and can organize diverse teams around new initiatives. Let's talk about the types of graphs and charts that you can use to grow your business.
Channels like social media or blogs have multiple sources of data and when you manage these complex content assets it can get overwhelming. What should you be tracking? What matters most? How do you visualize and analyze the data so you can extract insights and actionable information?
Do you want to convince or clarify a point? Are you trying to visualize data that helped you solve a problem, or are you trying to communicate a change that's happening?
A chart or graph can help you compare different values, understand how different parts impact the whole, or analyze trends. Charts and graphs can also be useful for recognizing data that veers away from what you’re used to or help you see relationships between groups.
Clarify your goals, then use them to guide your chart selection.
Different types of charts and graphs use different kinds of data. Graphs usually represent numerical data, while charts are a visual representation of data that may or may not use numbers.
So, while all graphs are a type of chart, not all charts are graphs. If you don't already have the kind of data you need, you might need to spend some time putting your data together before building your chart.
Most businesses collect numerical data regularly, but you may need to put in some extra time to collect the right data for your chart. Besides quantitative data tools that measure traffic, revenue, and other user data, you might need some qualitative data.
These are some other ways you can gather data for your data visualization:
Choosing the wrong visual aid or defaulting to the most common type of data visualization could cause confusion for your viewer or lead to mistaken data interpretation.
But a chart is only useful to you and your business if it communicates your point clearly and effectively.
To help find the right chart or graph type, ask yourself the questions below.
Then, take a look at 14 types of charts and graphs you can use to visualize your data and create your chart or graph.
Charts and graphs are perfect for comparing one or many value sets, and they can easily show the low and high values in the data sets. To create a comparison chart, use these types of graphs:
Use this type of chart to show how individual parts make up the whole of something, like the device type used for mobile visitors to your website or total sales broken down by sales rep.
To show composition, use these charts:
Distribution charts help you to understand outliers, the normal tendency, and the range of information in your values.
Use these charts to show distribution:
If you want to know more information about how a data set performed during a specific time period, there are specific chart types that do extremely well.
You should choose a:
Relationship charts can show how one variable relates to one or many different variables. You could use this to show how something positively affects, has no effect, or negatively affects another variable.
When trying to establish the relationship between things, use these charts:
Download this free data visualization guide to learn which graphs to use in your marketing, presentations, or project -- and how to use them effectively.
To better understand each chart and graph type and how you can use them, here's an overview of graph and chart types.
A bar graph should be used to avoid clutter when one data label is long or if you have more than 10 items to compare.
Bar graphs can help you compare data between different groups or to track changes over time. Bar graphs are most useful when there are big changes or to show how one group compares against other groups.
The example above compares the number of customers by business role. It makes it easy to see that there is more than twice the number of customers per role for individual contributors than any other group.
A bar graph also makes it easy to see which group of data is highest or most common.
For example, at the start of the pandemic, online businesses saw a big jump in traffic. So, if you want to look at monthly traffic for an online business, a bar graph would make it easy to see that jump.
Other use cases for bar graphs include:
Use a column chart to show a comparison among different items, or to show a comparison of items over time. You could use this format to see the revenue per landing page or customers by close date.
While column charts show information vertically, and bar graphs show data horizontally. While you can use both to display changes in data, column charts are best for negative data.
For example, warehouses often track the number of accidents that happen on the shop floor. When the number of incidents falls below the monthly average, a column chart can make that change easier to see in a presentation.
In the example above, this column chart measures the number of customers by close date. Column charts make it easy to see data changes over a period of time. This means that they have many use cases, including:
A line graph reveals trends or progress over time and you can use it to show many different categories of data. You should use it when you chart a continuous data set.
Line graphs help users track changes over short and long periods of time. Because of this, these types of graphs are good for seeing small changes.
Line graphs can help you compare changes for more than one group over the same period. They're also helpful for measuring how different groups relate to each other.
A business might use this type of graph to compare sales rates for different products or services over time.
These charts are also helpful for measuring service channel performance. For example, a line graph that tracks how many chats or emails your team responds to per month.
A dual-axis chart allows you to plot data using two y-axes and a shared x-axis. It has three data sets. One is a continuous set of data and the other is better suited to grouping by category. Use this chart to visualize a correlation or the lack thereof between these three data sets.
A dual-axis chart makes it easy to see relationships between different data sets. They can also help with comparing trends.
For example, the chart above shows how many new customers this company brings in each month. It also shows how much revenue those customers are bringing the company.
This makes it simple to see the connection between the number of customers and increased revenue.
You can use dual-axis charts to compare:
An area chart is basically a line chart, but the space between the x-axis and the line is filled with a color or pattern. It is useful for showing part-to-whole relations, like showing individual sales reps' contributions to total sales for a year. It helps you analyze both overall and individual trend information.
Area charts help show changes over time. They work best for big differences between data sets and also help visualize big trends.
For example, the chart above shows users by creation date and life cycle stage.
A line chart could show that there are more subscribers than marketing qualified leads. But this area chart emphasizes how much bigger the number of subscribers is than any other group.
These types of charts and graphs make the size of a group and how groups relate to each other more visually important than data changes over time.
Area graphs can help your business to:
Use this chart to compare many different items and show the composition of each item you’re comparing.
These graphs are helpful when a group starts in one column and moves to another over time.
For example, the difference between a marketing qualified lead (MQL) and a sales qualified lead (SQL) is sometimes hard to see. The chart above helps stakeholders see these two lead types from a single point of view– when a lead changes from MQL to SQL.
Stacked bar charts are excellent for marketing. They make it simple to add a lot of data on a single chart or to make a point with limited space.
These types of graphs can show multiple takeaways, so they're also super for quarterly meetings when you have a lot to say, but not always a lot of time to say it.
Stacked bar charts are also a smart option for planning or strategy meetings. This is because these charts can show a lot of information at once, but they also make it easy to focus on one stack at a time or move data as needed.
You can also use these charts to:
Also known as a Marimekko chart, this type of graph can compare values, measure each one's composition, and show data distribution across each one.
It's similar to a stacked bar, except the Mekko's x-axis can capture another dimension of your values— instead of time progression, like column charts often do. In the graphic below, the x-axis compares each city to one another.