When discussing data visualization, among the most common questions we are asked are “What are the different types of charts” and “What are the different kinds of graphs” that can best articulate their message. To help you get a sense of which graphs and charts are best suited in the data visualization solutions, here are some of the visualization categories.
1) Temporal
Data visualizations fall under the “temporal” category if they satisfy two conditions: (1) that they are linear, and (2) that they are one-dimensional. In other words, they usually feature lines which can either stand alone or intersect or overlap with each other.
The benefit of choosing temporal data visualization types such as scatter plots, polar area diagrams, time series sequences, timelines, and line graphs, is that they are familiar. Most of us have been exposed to these charts in school and the workplace, which means they instantly “click” when we read them.
2) Hierarchical
Hierarchical data visualizations are those that order groups within larger groups. The classic “tree diagram” is one of the most popular types of visual hierarchical data because it is simple. And, the linear path makes it a breeze to follow along. These figures can be arranged to flow from left to right or top to bottom.
Other forms of hierarchical data plots are ring charts, treemap charts, sunburst diagram graphs, circle packing, and dendrogram charts. These data visualization formats are best suited for those looking to display clusters of information, especially that which emanates from a single origin point.
The downside, however, is that these graphs tend to be more complicated than most.
3) Network
Data does not exist in a vacuum. Instead, many datasets connect deeply with others, and the ability to demonstrate these relationships using visual network data gives you an edge over long-winded explanatory text. This data type allows users to communicate the intricate networks that link one data set to another.
A few of the most common network data visualization types are alluvial diagram charts, node-link diagrams, network diagram charts, word cloud plots, non-ribbon chord diagram plots, parallel coordinates plot, and matrix charts.
If you want to show relationships between data sets, these are the visualizations that you should lean on.
4) Multidimensional
Multidimensional data have multiple dimensions, which implies that there are at least two variables at play. Often, multidimensional data makes for the most eye-catching visuals because of the many coinciding layers and datasets.
When it comes to multidimensional visual data, the most common varieties are:
- Pie charts
- Histograms
- Scatter plots
- Timetable charts
- Venn diagrams
- Steam graphs
- Stacked area graphs
- Stacked bar graphs
- Parallel sets
- Multi-set bar charts
These chart types are among the most useful because they can take big data and boil them down to their fundamental takeaways.
5) Geospatial
Geospatial and spatial data relays information related to real-life physical locations. A personal favorite of many, geospatial maps overlay familiar territorial maps with data points. These maps are commonly used to showcase sales or acquisitions made over time. Plus, they’re visually appealing if properly color coordinated.
Some of the most popular geospatial visual data types are the cartogram, choropleth, dot distribution map, flow map, density plot, heat map, and connection map.
Most viewers recognize these maps for their widespread use in political campaigns and by multinational corporations that want to visualize their market penetration.
How Do You Know Which Graph to Use?
There are countless graphs and charts to choose from when converting raw data into a visual format. For many, it can be difficult to decide whether to rely on a straightforward pie chart or bar graph or to opt for a more spatially complex chart type.
Two common questions we often hear are: “How do you know which chart to use?” and “Which graph should be used?” The answer, as always, depends on the experience level and familiarity with big data analytics.
Know Your Audience
The first step in creating an effective data visualization strategy is to identify your audience with precision. If your audience consists of experienced, executive-level marketers, chances are they have already been exposed to hundreds of bar charts, graphs, and other visualizers.
Audience familiarity is a key factor when deciding which types of data visualization, you should include in your content. For those who are well-versed in visual data, such as senior marketers or C-suite executives, you can employ a variety of visualization methods to help break the mold and introduce new, stimulating insights.
For less experienced general audiences who aren’t as familiar with data analytics, the following types of data visualizations are recommended:
- Dot map
- Pie graph
- Linear line graph
- Flowchart
- Multi-set bar graph
- Word map
On the other hand, there are several more sophisticated kinds of visual data that can communicate more information than those listed above. If you are preparing a report for an experienced industry client or readership, then the following are suggested:
- Stacked bar graph
- Spiral plot
- Stacked area graph
- Point and figure chart
- Choropleth map
- Candlestick chart
Know Your Colors and Branding
For decades, marketers have understood that there is a clear link between color and conversions when it comes to establishing a brand identity. But what is less understood, however, is that data is part of one’s brand too.
When visualizing data, it is equally important to keep brand color schemes and aesthetic patterns in check. Bar and pie charts, as well as geospatial data types, are excellent opportunities to include plenty of color in your visualization designs.
By keeping colors on-brand, the reader can better retain both the information and your brand identity.