What is data visualisation? (Definition, types and tools)

By Indeed Editorial Team

Updated 11 August 2022 | Published 3 January 2022

Updated 11 August 2022

Published 3 January 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Collecting, analysing and presenting information may be an important part of your job regardless of the industry you're in. There are several ways to present information clearly, including data visualisation. Learning how to use graphics to display data sets can help you become a more effective communicator and presenter in the workplace. In this article, we explain what data visualisation is, discuss why it's important and explore different types of techniques to visualise data.

What is data visualisation?

The answer to 'What is data visualisation?' is a technique that allows you to visually present data using charts, graphs, tables, infographics, maps or other illustrations. It has two primary purposes, analysis and communication. Data visualisation methods and tools help you clearly present trends, patterns, outliers and other critical information from a data set. By learning how to clearly and effectively present large data sets in a visual form, you can make data more accessible and easier to understand for others.

Related: How to become a data analyst

Why is data visualisation important?

Data visualisation is a key element of all industries, including most roles within every large company. It can provide a number of benefits to a workplace, including:

  • Clarifying complex data: Analysing, presenting and understanding data can be challenging. By using appropriate data visualisation techniques, you can better communicate data trends or patterns to larger audiences.

  • Strengthening a pitch: Most people find it easier to understand complicated concepts when they can see them through shapes, colours or other visual elements. By including graphs, timelines or charts in your presentations, you may increase your chances of convincing someone to take certain actions.

  • Adding a personal touch to presentations: Including data visualisation elements in your presentation or report can demonstrate that you're capable of making the information more accessible. It also teaches you how to gradually improve the way you organise the information.

  • Improving decision-making: Analysing large sets of data can be time-consuming and it's usually impossible to effectively present raw chunks of data during client or department meetings. Learning basic data visualisation techniques and using them when your client or team is about to make an important business decision helps you improve the speed and effectiveness of their decision-making process.

Related: How to become a data scientist in 4 steps

Types of data visualisation

There are various data visualisation tools and techniques that you may consider depending on the data set you're working with. Consider these common types of data visualisation:

Temporal

Temporal visualisation elements are one-dimensional, linear charts that highlight the start and finish of an item or set of items. Analysts commonly use them to track medical records, demonstrate historical progression and plot a basic timeline for projects. Here are some examples of temporal visualisation elements:

  • scatter plots

  • timelines

  • line graphs

  • Gantt charts

Hierarchical

Hierarchical tools are collections of items where each item has a connection to its parent item. When presenting data this way, you can give items multiple attributes. A common example of hierarchical data visualisation is the file system of a computer. Other hierarchical tools are:

  • tree diagrams

  • sunburnt diagrams

  • decision tree

  • flow charts

Network

Network tools are tools that allow you to visualise data that's hard to capture using a tree structure. It allows you to present the connection between items and highlight the shortest, or most cost-effective, paths that connect them. Common network visualisation tools include:

  • node-link diagrams

  • matrix

  • word count

  • dependency graphs

Plantar

Also known as geospatial or spatial tools, plantar visualisation tools are two-dimensional layers that allow you to take into consideration relationships between physical locations and the data you collected. A key element in visualising data this way is geospatial analysis. It involves gathering, displaying and manipulating maps or satellite images to integrate interactive visualisations into them. You may use these tools to display sales data or voter information during a campaign. Here are common plantar tools:

  • flow maps

  • density maps

  • heat maps

Multidimensional

Multidimensional visualisation tools are 3D graphs and charts that use multiple variables to categorise data. There are two subtypes of these visualisations. One takes into consideration category counts or proportions, such as pie charts, stacked bar graphs or histograms. The second subtype focuses on the relationship between variables, in a form of Venn diagrams, step charts or waterfall charts.

Related: A guide to the different types of charts and graphs

Key characteristics of data visualisation

Choosing the right data visualisation technique or tool allows you to make your data both useful and usable. Prior to building your visualisation, consider these characteristics that effective data visualisation has:

  • It's accessible: When you make your data visualisation accessible, more people can use it to gain insight into a project or a report. It's important to make it accessible on any device and allow people to modify and filter through the data whenever it's necessary.

  • It's scalable: Successful data visualisation may help many people during their research and they may want to use it in their reports or projects. It's essential that you make your visualisation scalable, so others can perform maintenance or modify it.

  • It's visually appealing: It's easier for the human brain to understand complex concepts when you present them in an organised, visual way that's appealing to the audience. Using different shapes and colours to represent individual items or a group of items can help you highlight the most relevant information.

  • It's informative: When building your data visualisation, it's important that you take into consideration your audience's position, context and needs. Delivering information that's essential to them eliminates distractions or confusion and makes the visualisation more useful to others.

How to choose the right data visualisation tool

Depending on what kind of data you've collected and analysed, you may choose different visualisation tools to present it. Consider these tips to decide:

1. Consider the size and diversity of your data

The size and diversity is the most important element that you can consider when choosing visualisation tools and techniques. For example, if you're working with a simple data set with few variables, you may consider basic tools, such as graphs or pie charts. If your goal is to present relationships between variables and how to get from one item to another, you may consider dependency graphs.

Related: Analytical skills: definitions and examples

2. Determine what you want to emphasise

Determining the central concepts you want to emphasise is essential to making your visualisation informative and more accessible to others. It also influences which tools you may use to highlight that. For example, you can focus on highlighting sales growth rates or customer preferences.

3. Understand your audience

Determine who you're presenting your information to, for example, a client who's new to the topic or your colleagues, who are familiar with the basics of the data you're visualising. This information is essential to choosing the right tools for your presentation or report. For example, public audiences may find it easier to view and understand simpler visualisations and they typically require more clarification in a textual form. Similarly, your colleagues who work in the same department as you're more likely to be familiar with more advanced visualisations.

Related: How to start a presentation and keep the audience interested

4. Always opt for the simpler option

Even if you know that your audience can understand more complex graphs and other visualisations, it's usually a better idea to choose the simplest option available to you. You can narrow down what data story you want to tell during the meeting. You may consider creating a complex visualisation first and developing its simpler variation. This way, when someone requests to see the full picture of what you were presenting, you'd have it ready to send to them.

5. Focus on the emotional impact you want to have

Some visualisations convey the same message with equal clarity, but they may have different emotional impacts. For example, if you want to convey the excitement of financial growth, you might use a line graph that continues charting upward instead of a bar chart showing increasing numbers. Although it may seem insignificant, in reality, it's a powerful technique that may help you motivate your team.

6. Get feedback

Consider creating several versions of your visualisations and asking a colleague to review them for you. It's important that you think of someone who's familiar with the concept you're working on, as they may help you decide which technique to use when presenting your data analysis to a larger audience. You may even create a short questionnaire that they'd fill in after reviewing the options you've prepared, for example, to rate the emotional reaction that they have viewing each graph or timeline.

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