How to Choose the Right Data Visualization

1) Choosing the right data visualization is crucial for effectively communicating complex information. Each type of chart serves specific purposes: comparison, proportion, trend, or distribution, enabling users to select the most appropriate visual representation for their data. 

2) Employing the correct visualization enhances understanding, facilitates deeper insights, and supports more informed decision-making in business and research. Proper visualization selection is key to accurately reflecting the underlying data and achieving communication objectives effectively.

3) There are various kinds of charts. How to choose the right chart to convey information clearly and concisely, This article divides the charts into several categories, namely comparison, proportion, trend or association, and distribution. You can choose the appropriate chart according to the purpose.

Column Chart

Introduction: A regular column chart uses vertical columns to display numerical comparisons between categories. One axis of the column chart shows the category being compared, while the other axis represents the corresponding scale value.

Features: It is not suitable for comparing data of more than ten categories. And it is recommended to use a bar chart when the category label is too long.

Contrast Column Chart

Introduction: A contrast column chart uses forward and backward columns to display the numerical comparison between categories. One axis of the chart shows the category being compared, while the other axis represents the corresponding scale value.

Features: It is used to display the comparison of data with opposite meanings. If not, it is recommended to use grouped column chart.

Grouped Column Chart

Introduction: Grouped column charts are often used to compare different types of data under the same group. Column height is used to display numerical comparisons, and color is used to distinguish different types of data.

Features: Under the same group, there should not be too many categories of data.

Stacked Column Chart

Introduction: Stacked column charts can be used to compare total amounts between groups, as well as to view the size and proportion of each small category contained in each group. Therefore, it is suitable for dealing with the relationship between parts and the whole.

Features: It is suitable for displaying the total size, but not for comparing the same category under different groups.

Partitioned Line Chart

Introduction: Partitioned line charts can separate multiple indicators and reflect the trend of things changing with time or order categories.

Features: It is suitable for comparing trends, avoiding multiple line charts crossing each other.

Radar Chart

Introduction: A radar chart is also called a spider web map. Each of its variables has an axis that emits outward from the center. The angles between all axes are equal, and each axis has the same scale.

Features: Too many variables in a radar chart will reduce the readability. It is suitable for displaying performance data.

Word Cloud

Introduction: Word cloud is an important way to visualize text big data. It is often used to highlight high-frequency sentences and vocabulary in large amounts of texts to quickly perceive the most prominent words. It is commonly used for statistical analysis of high-frequency search fields on websites.

Features: It is not suitable for processing text data with a large volume, nor for data processing with low data differentiation.

Aggregate Bubble Chart

Introduction: In an aggregate bubble chart, dimensions define each bubble, and measures define the size and color of the bubble.

Features: It is not suitable for processing data with low differentiation.

Rose Chart

Introduction: The role of a rose chart is similar to a column chart. It is mainly used for comparison. The size of the values is mapped to the radius of a rose chart.

Features: When the data is similar, it is not suitable to use a pie chart, but rather a rose chart.

Proportion

Pie Chart

Introduction: A pie chart generally uses color to differentiate between categories, and compares data by the size of the amplitude. It can display the proportion between each category and the overall data.

Features: The number of categories cannot be too large. And it is not suitable for data with low differentiation.

Rectangular Tree

Introduction: A rectangular tree is suitable for presenting data with hierarchical relationships and can intuitively reflect comparisons between the same levels. Parent nodes nest child nodes. Each node is divided into rectangles of different sizes, using the size of the area to display the attributes corresponding to the node.

Features: It is suitable for weighted tree data, comparing the size of each category and the proportion of target data in the overall data.

Percent Stacked Column Chart

Introduction: A percent stacked column chart is used to compare the proportions of different categories in the same grouped data.

Features: The number of different categories within the same group cannot be too large.

Multi-layer Pie Chart

Introduction: A multi-layer pie chart refers to a pie chart with multiple layers and a hierarchical relationship between layers. Multi-layer pie charts are suitable for displaying complex tree-structured data with parent-child relationships, such as geographic area data, company hierarchies, quarterly or monthly time hierarchies, and so on.

Features: There should not be too many levels or categories. Too many will cause too small slices, which affects readability.

Dashboard

Introduction: You can set target values in the dashboard and use it to show speed, temperature, progress, completion rate, satisfaction, and so on. In many cases, it is also used to indicate proportion.

Features: It is only suitable for data display of a single indicator.

Trend or Association

Line Chart

Introduction: Line charts are convenient for showing trends in how things change over time or other ordered categories. 

1. It can be used to analyze the interaction and mutual influence of multiple sets of data over time so as to draw some conclusions and experience. 

2. It can be used to compare the size of multiple sets of data at the same period.

Features: The number of lines should not be too many, as it may lead to poor readability of the chart.

Range Area Chart

Introduction: Range area charts are used to show continuous data, which can well represent trends, accumulation, decline, and change.

Features: It can show the changing trend of the difference value between two continuous variables.

Regular Area Chart

Introduction: A regular area chart evolves from a line chart, and is also convenient for reflecting the changing trend over time or other ordered categories. Because of the area filling, it can better reflect the changing trend than a line chart.

Features: Preferably no more than five area lines.

Scatter Chart

Introduction: A scatter chart can show the shape of a data cluster and analyze the distribution of the data. By observing the distribution of the scattered points, you can infer the correlation between variables, which can be achieved through data fitting in FineBI.

Features: A scatter chart can better reflect data distribution when there is a relatively large amount of data.

Waterfall Chart

Introduction: Waterfall charts show cumulative summary when values are added or subtracted. And it is usually used to analyze the impact of a series of positive and negative values on the initial value (such as net income).

Features: Data changes can be more intuitively displayed in a floating column chart.

Distribution

Scatter Chart

Introduction: A scatter chart can show the shape of a data cluster and analyze the distribution of the data. Observe the distribution of scattered points to infer the correlation of variables.

Features: A scatter chart can better reflect data distribution when there is a relatively large amount of data.

Heatmap

Introduction: A heatmap highlights the weight information of each point within the coordinate range in a special way.

Features: The effect of a heatmap is softened and is not suitable for precise data display. It is mainly used for viewing distributions.

Map

Introduction: The map component instantly reflects data on geographic locations. FineBI provides various map components, including thermal map, area map, flow map and point map.

Features: Maps allow you to observe data relationships intuitively in different regions.

Funnel Chart

Introduction: The funnel chart is also called an inverted triangle diagram. A funnel chart has a logical order from top to bottom and is often used for process analysis, such as analyzing which step has an abnormal dropout rate.

Features: There must be a logical order from the top to bottom. If there is no logical relationship, it is recommended to use a column chart for comparison.

In conclusion, selecting the appropriate data visualization is crucial for effective communication and analysis. Understanding the specific characteristics and intended uses of various chart types, such as those for comparison, proportion, trend, association, and distribution, enables users to present data more clearly and meaningfully. Proper chart selection not only enhances the readability of data but also significantly impacts the decision-making process by providing deeper insights.