Data Visualisation: Definition, Benefits, and Examples

Data visualisation is a powerful way for people, especially professionals, to display data so those looking to gain insights can interpret it easily. It helps tell a story with data by turning spreadsheets of numbers into stunning graphs and charts.

Discover data visualisation, including definitions, benefits, examples, types, and tools. Additionally, explore the skills you’ll need and online courses you can take from anywhere to make implementing data visualisation in your work easier. 

What is data visualisation?

Data visualisation refers to the representation of information and data using charts, graphs, maps, and other visual tools. These visualisations allow us to quickly understand any patterns, trends, or outliers in a data set.

Data visualisation also makes data accessible to the general public or specific audiences without technical knowledge. For example, the NHS might provide a map of vaccinated regions.

Data visualisation helps drive informed decision-making and adds colourful meaning to an otherwise bland database.

Benefits of data visualisation

Data visualisation can be used in many contexts in nearly every field, like public policy, finance, marketing, retail, education, sports, history, and more. Check out a few of the primary advantages of data visualisation:

• Storytelling: Colours and patterns in clothing, arts and culture, architecture, and other areas attract attention. Data is no different—colours and patterns allow us to visualise the story within the data.

• Accessibility: Visualisation enables you to share information in an accessible, easy-to-understand manner for various audiences.

• Visualise relationships: When viewing information in a graph or chart, it’s easier to spot the relationships and patterns within a data set.

• Exploration: More accessible data means more opportunities to explore, collaborate, and inform actionable decisions.

Data visualisation and big data

Companies collect “big data” and synthesise it into information. Data visualisation helps portray significant insights, like a heat map illustrating regions where individuals seek mental health assistance. You can use visualisation software with data-collecting software to synthesise all that data.

Tools for visualising data

With so many data visualisation tools available, finding one to suit your needs should be simple. Before committing to a tool, consider researching whether you need an open-source site or could simply create a graph using Excel or Google Charts. The following are standard data visualisation tools that could suit your requirements. 

• Tableau

• Google Charts

• Dundas BI

• Power BI

• JupyteR

• Infogram

• ChartBlocks

• D3.js

• FusionCharts

• Grafana

Get started with a free tool

Visual representations to illustrate data can be immensely powerful regardless of the field. Tableau has a free public tool that anyone can use to create stunning visualisations for a university project, non-profit, or small business. 

Types of data visualisation

Visualising data can be as simple as a bar graph or scatter plot but becomes powerful when analysing, for example, the median age of UK members of parliament vis-a-vis the median age of the UK population. The following list offers some standard types of data visualisations:

• Table: A table is data displayed in rows and columns, which you can easily create in a Word document or Excel spreadsheet.

• Chart or graph: This type presents information in tabular form with data displayed along an x and y axis, usually with bars, points, or lines, to represent data in comparison. An infographic is a special chart that combines visuals and words to illustrate the data.

• Gantt chart: A Gantt chart is a bar chart that portrays a timeline and tasks specifically used in project management.

• Pie chart: A pie chart divides data into percentages featured in “slices” of a pie, all adding up to 100 percent. 

• Geospatial visualisation: This visualisation depicts data in map form with shapes and colours that illustrate the relationship between specific locations, such as a choropleth or heat map.

• Dashboard: Data and visualisations are displayed, usually for business purposes, to help analysts understand and present data.

Data visualisation examples

You can create various types of charts and graphs to illustrate essential data when using data visualisation tools. These are a few examples of data visualisation in the real world:

• Data science: Data scientists and researchers have access to libraries using programming languages or tools such as Python or R, which they use to understand and identify patterns in data sets. Tools help these data professionals work more efficiently by coding research with colours, plots, lines, and shapes.

• Marketing: Tracking web traffic and social media analytics data can help marketers analyse how customers find their products and whether they are early adopters or laggard buyers. Charts and graphs can synthesise data to help marketers and stakeholders better understand these trends.

• Finance: Investors and advisors who buy and sell stocks, bonds, dividends, and other commodities analyse the movement of prices over time to determine which are worth purchasing for short—or long-term periods. Line graphs help financial analysts visualise this data, toggling between months, years, and even decades.

• Health policy: Policymakers can use choropleth maps, which divide into geographical areas (nations, states, continents) by colours. They can, for example, use these maps to demonstrate the mortality rates of cancer or ebola in different parts of the world. 

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Jobs that use data visualisation

From marketing to data analytics, data visualisation is a skill that can benefit many industries. Building your skills in data visualisation can help in the following jobs:

• Data visualisation analyst: As a data visualisation analyst (or specialist), you’d be responsible for creating and editing visual content such as maps, charts, and infographics from large data sets. 

• Data visualisation engineer: Data visualisation engineers and developers are experts in both manoeuvring data with SQL, as well as assisting product teams in creating user-friendly dashboards that enable storytelling.

• Data analyst: A data analyst collects, cleans, and interprets data sets to answer questions or solve business problems.

Data is everywhere. In creative roles such as graphic designer, content strategist, or social media specialist, data visualisation expertise can help you solve challenging problems. As an email marketer, you could create dashboards to track analytics or make infographics as a communications designer.

Conversely, data professionals can benefit from data visualisation skills to tell more impactful stories through data.

Dive into data visualisation

Data visualisation is a powerful tool for representing information and data in a way that is easy for yourself and others to understand. It allows people to gain insights into data and make informed decisions.

Continue learning and get a solid foundation in the basics of data visualisation with the University of California Davis’ Data Visualisation with Tableau Specialisation. You’ll leverage Tableau’s library of resources to learn best practices for data visualisation and storytelling, learning from real-world and journalistic examples. Tableau is one of the most respected and accessible data visualisation tools. 

To learn more about data visualisation using Excel and Cognos Analytics, look at IBM’s Data Analysis and Visualisation Foundations Specialisation.