Data visualization helps people comprehend and attain insight into big data. It represents complex data in visually interesting ways that assist in our understanding, and paves the way for a greater link between the provided raw data, and our overall engagement with it.
Nowadays, we accumulate data in ever-increasing sizes, so we need an intelligent way to understand such vast volumes of information. In analytics, we often use different types of data visualization to convey complex datasets. Do you ever wonder how useful it really is?
In this blog, we cover the top 10 essential types of data visualization you need to have in yor solution today, and which general use cases they best fit - with illustrative examples.
What are the Prevalent Techniques of Data Visualization?
Firstly, data visualization helps businesses dive deeper into data exploration, analyze hypotheses, and communicate results effectively. Moreover, it helps people detect patterns, catch trends, and find correlations in data that numbers alone can't convey.
Are you overwhelmed by a vast landscape of data visualizations and unsure which one to choose? We will help you by listing their details.
Let us take you on a whirlwind tour of popular data visualization techniques.
Related: The Role of Data Visualization in Business Intelligence: What Is It?
1) Line Graph
An example of a line graph, created in Yellowfin.
A line graph demonstrates the values of different categories over time. Specifically, it shows changes in value across continuous measurements of items. It illustrates an overall trend leaving no room for confusion. That's why people use it for several business use cases.
Overall trends help business leaders forecast projections for future outcomes. When the line moves up, it often shows positive changes. On the other hand, the movement of the line going down shows negative changes. It proves handy when you explicitly want to show trends for multiple categories over the same course of time.
2) Column Chart
An example of a column chart, created in Yellowfin.
One data visualization technique we frequently see is a column chart. People use it to compare different values side by side. Using it is a great idea when you want to pay attention to total figures instead of the shape of a trend.
A column chart is quite popular as it is simple to understand and can compare diverse kinds of data. It often shows the time on the horizontal axis, while the vertical axis displays values. Note that a column and line chart combination is a good choice when showing figures and an overall trend.
3) Bar Graph
An example of two comparative bar graphs, created in Yellowfin.
Another visualization method is a bar graph, also called bar chart, that indicates the values by the length of the bar. The other axis, meanwhile, shows the categories that are supposed to be compared. We can draw a bar chart both vertically and horizontally. Horizontal bar charts are a good choice when plotting multiple bars.
At one glance, a bar chart helps us contrast data sets from several groups while exhibiting the relationship between two axes. Bar charts also show changes in data over time.
4) Pie Chart
An example of a pie chart, created in Yellowfin.
Another common data visualization technique is pie charts. As a circular graph, it shows data of relative sizes through pie slices. It serves various application purposes, including showing percentages of customer types, product revenues, and country profits. It is simple to grasp, and for this reason, people employ it to demonstrate relative sizes.
Pie charts work well to display percentages as they show each element as part of a whole. The whole pie, nevertheless, shows one hundred percent of the total. The pie slices symbolize different parts of the pie chart. However, it is not a good choice when you want to display complex information for a thorough explanation.
5) Funnel Chart
An example of a funnel chart, created in Yellowfin.
A funnel chart is the type of data visualization people often use in multiple business contexts. It helps track users in a pipeline flow; for example, for sales, it specifically shows the decreasing values as customers go through the sales funnel.
The funnel width displays the number of users that make their way at each step. It shows a linear process comprising sequential stages and a swift picture of where people drop out of the process.
6) Map-based Plot
Map-based plot is another type of data visualization technique that helps show geographically related data. It is a useful method when you want to plot a dataset that corresponds to actual geographic locations. Instead of plotting values, it shows value by filling regions with color on a map.
Its data expression is crystal clear, intuitive, and presents data in the form of maps. Readers can read the distribution of data in each region, so it brings convenience to make better decisions. The aesthetic element is another significant reason to use it. It transforms boring content into eye-catching content when you equip it with an aesthetically-appealing map.
7) Heat Map
An example of a heatmap, created in Yellowfin.
Heat map displays values on two variables of interest. While the axis variable can turn out to be categorical or numeric, the grid comes into shape by splitting each variable into several levels. It shows differences in data in the form of color variations.
The values of grid cells are colored, with darker colors often indicating higher values. Colors help communicate values to the viewer so they can identify trends quicker. Hence, interpreting a heatmap is easy.
8) Waterfall Chart
An example of a waterfall chart, created in Yellowfin.
A waterfall chart visually shows the overall growth or decline in value between two specified points. Its goal is to show how a value has risen or declined over time.
It dis-aggregates and visualizes different distinctive components that contribute to the net change instead of reflecting starting and ending values in two bars.
9) Scatter Plot
An example of a scatter plot, created in Yellowfin.
Another data visualization technique is a scatter plot. While circle color presents categories of data, circle size represents the volume of the data. We represent the data for two variables by points against the vertical and horizontal axis.
The purpose of a scatter plot is to show the relationship between the provided variables, which, in turn, helps identify trends or correlations in data. The usefulness of scatter plots emerges when the data is significantly large, as identification of trends is possible only in the presence of extensive data points.
10) Pictogram Chart
Regarding data visualizations, a pictogram is another type that uses icons and images to represent data. It presents simple data aesthetically engagingly, using repeated icons to show simple data.
Apart from making the data engaging, it also proves handy in situations when cultural differences emerge as a hurdle to making the audience understand the data. Remember that a pictogram is not a good choice for large data sets as it becomes difficult to count.
What are Other Options for Data Visualization?
Data visualization is a powerful tool that may help you become a better communicator in your reports and dashboards.
Although the techniques discussed above are some of the most popular, there are many more types of data visualization available to use.
Other methods of visualizing information include:
Correlation matrix
Bubble charts
Cartograms
Circle views
Network diagrams
Dendrograms
Dot distribution maps
Open-high-low-close charts
Word clouds
Polar areas
Radial trees
Ring Charts
Choropleth maps
Sankey diagrams
Span charts
Streamgraphs
Bullet graphs
Treemaps
Wedge stack graphs
Violin plots
Highlight tables
Timeline
Support for these various types of visualizations come down to the business intelligence (BI) and analytics solution you adopt, with certain modern vendors offering extensive data visualization tools - including Yellowfin.
Best Enterprise Data Visualization Tool for Charting
While other solutions in the market have simple visualization options, Yellowfin provides a wide range of different types of data visualization and customization, with over 50 types of charts. In addition, it provides a flexible design canvas for novices and expert designers alike. Unlike the limited functionality offered by solutions in the market, Yellowfin provides a complete package.
Thankfully, white-labeling and integration with native apps is also possible. It also incorporates location intelligence, such as in-built mapping ability and Google Maps API for visuals. Furthermore, it carries innovative charts & supports JavaScript charting libraries that include D3 and three.js.
To learn more about Yellowfin's data visualization product suite, check out how our BI solution works for various types of data visualization. It also supports them out-of-the-box for corporate and product teams.
