What Is Data Visualisation? Importance, Types And How To

Data visualisation is a valuable technique that a professional uses to communicate and interpret information to colleagues and clients. Companies use data visualisation techniques, like charts and graphs, to convey information effectively. If you have an interest in visualising data or presenting visual data, knowing more about how to use it can help in a fulfilling career. In this article, we answer “What is data visualisation?”, explore its importance and types, understand when to use it and discover ways of ensuring better data visualisation.

What is data visualisation?

The answer to the question “What is data visualisation?” is that it is a practice of presenting data as graphics, images and tables, infographics and other patterns. Using the data visualisation technique, the company can find patterns, trends, outliers and other critical information from a data set.

Graphical representation of data helps deliver information to unfamiliar people with the context, terms and implications of data presented. Using the data visualisation technique, an analyst inputs a data set in software and uses various charting and visualisation tools to convert data into graphics that highlight the point a company wants to prove.

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Why is data visualisation important?

Data visualisation is essential because:

• Makes data more accessible: Visualising data can help you better communicate important information about your data. You can communicate information to people with little or no familiarity with statistics and data analysis.

• Clarifies complex data: Finding trends in large data sets can be challenging, but not with visualisation. It helps in breaking down complex data into easy-to-understand information.

• Examines the market: Data visualisation takes information from different markets and provides insights that help the audience avoid irrelevant information while absorbing relevant information. It helps you provide opportunities within those markets by displaying data on various charts.

• Strengthens a persuasive argument: Using data can be helpful to prove a point during a persuasive argument. You can convey a story to your customer and use the data to persuade your audience to become your customers.

• Improves decision making: Though data visualisation is a time-consuming technique, it can help you make important decisions. Also, visualising data can help you speed up the decision-making process.

• Allows you to find errors: Visualising your data can help identify errors in a data set. Visualisation can help you identify erroneous data and remove them from your analysis.

• Grasps the latest trends: Data visualisation helps you discover the latest business trends that can increase profits.

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Types of data visualisation

You can choose to visualise your data in different formats and each format displays data that highlights information a company wants to empathise. Here are a few types of data visualisation techniques to use:

Tree or hierarchical data visualisation

This visualisation technique helps you organise a group of data within a group. For instance, you can create bubbles, branches of information and clusters that originate from a parent item. One of the most common types of hierarchical data visualisation is the file and folder system found on the computer. Some common types of hierarchical tools to use are:

• Decision trees

• Tree diagrams

• Flow charts

• Sunburst diagrams

Temporal data visualisation

Temporal data visualisation is linear or one-dimensional linear visualisation. Temporal data has a start and finish time and data points may overlap. Usually, businesses use temporal data visualisation to represent time series data. Companies widely use this technique to report visual gains and losses of a company. Some common types of temporal data visualisation are:

• Bar charts

• Line charts

• Time series sequence

• Polar area diagrams

• Timelines

• Scatter plots

• Gantt charts

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Network data visualisation

Network data visualisation shows the relationship between various nodes and entities. One of the primary purposes is identifying the fastest path between groups and two items. Some examples of network data visualisation are:

• Node-link diagrams

• Word clouds

• Matrix charts

• Dependency graph

Geospatial data visualisation

Geospatial or spatial or planar data visualisation allows you to create a strong relationship with collected data and physical locations. Companies and government organisations may use geospatial data visualisation to show voters' information on a map during a political campaign. Also, companies use it to represent sales data in specific regions. This technique overlays familiar maps with data points. Some examples of geospatial data visualisation are:

• Cartogram

• Heat maps

• Flow maps

• Density maps

Multi-dimensional data visualisation

Multi-dimensional graphs and charts are 3D charts that use multiple concurrent data variables to categorise data. The dimensionality of this data type is the most vibrant and eye-catching visualisation type. When businesses filter the data, they use multi-dimensional visualisation to create vibrant and attractive visualisation. It breaks down your data into different ways to capture important information. Some examples of multi-dimensional data visualisation are:

• Venn diagrams

• Pie charts

• Stacked bar graphs

• Histograms

• Step charts

• Waterfall charts

When to apply data visualisation

While there is no right or wrong time for data visualisation, effective data visualisation can help you achieve various purposes that depend upon the following factors:

• For showing change over time: When businesses want to see a change in the value of a variable over time, they take help from data visualisation.

• For understanding data distribution: Another time companies visualise data is when they want to understand the distribution of data points' values. This usually occurs during the exploration stage, when a business wants to understand the properties of data features.

• For comparing values between groups: Another common reason for visualising data is comparing the values between two distinct groups. It helps in understanding data change.

• For observing relationships between variables: Understanding the relationship between data features is another reason companies use data visualisation. It helps in observing the trends and patterns between them.

How to better your data visualisation

Here are a few steps you can follow to better data visualisation:

1. Define a clear purpose

The first step towards good data visualisation is identifying the problem you are trying to solve or how your information would provide real value to the company. Having a purpose can avoid common problems in data visualisation. To understand the purpose of your data visualisation, it might be beneficial to know what your key performance indicators or KPIs are. While more data is not always beneficial, having the right data set can help you provide the right answer.

2. Know your target audience

Knowing your audience is essential because it helps you choose a data visualisation strategy that they understand. Having an answer to how the audience can process the information can help choose the right visualisation strategy. For instance, if you present data to internal team members, you can use complex graphics. When representing data to non-technical customers, you might prefer simple graphs to summarise the findings.

3. Keep your visualisation simple

After understanding your target audience, it is essential to keep your visualisation simple. The more complicated the visualisation technique you use, the more difficult it can become for your audience to consume information. Regardless of the size of the data you are visualising, use techniques that are simple to comprehend and understand.

4. Ensure the audience understands your visualisation

Ensure that you communicate information in a way that does not rely on colour or style. While colours are an essential aspect of data visualisation, allowing your data to depend upon colour for your audience to understand is unacceptable. Ensure your visualisations are inclusive so your audience understands graphics with or without colour.

5. Provide context

Your data visualisation is not going to serve any purpose unless you connect it to a broader context. Providing some context about why you interpret data and the problem you are trying to solve can make interpretation easier. It helps the audience understand why you present the data and what they can expect from the visualisation result.

How to choose the right tool for visualising the data

Use these steps to choose the right tool for data visualisation:

1. Consider the size of your data set: If you have a data set with a few variables, it might be beneficial to choose options like pie charts, bar graphs or line graphs.

2. Know the concept you want to emphasise: You can highlight the right elements like customer preference or growth over time using the right visualisation tools.

3. Know how your audience processes visual information: Understanding how your audience processes visual information is essential in choosing the right tool. Usually, customers prefer simple visualisation tools like line graphs or pie charts, while your colleagues might prefer complex visualisation of data, such as scatter plots or Venn diagrams.

4. Use a simple format: Based on the data you are trying to portray, choose options that are easy to understand. For instance, when showing sales numbers or employee retention over time, using a line graph or bar chart can help you represent information.

5. Receive feedback on your visualisation: Ask your colleagues to review it and ask for feedback. Ask them whether they can understand the information you are trying to communicate using the visualisation technique.