What is data visualisation?

Data visualisation, sometimes abbreviated to dataviz, is a step in the data science process. Once data has been collected, processed, and modelled, it must be visualised for patterns, trends, and conclusions to be identified from large data sets.

Used interchangeably with the terms ‘information graphics’, ‘information visualisation’ and ‘statistical graphs’, data visualisation translates raw data into a visual element. This could be in a variety of ways, including charts, graphs, or maps.

The use of big data is on the rise, and many businesses across all sectors use data to drive efficient decision making in their operations. As the use of data continues to grow in popularity, so too does the need to be able to clearly communicate data findings to stakeholders across a company.

The importance of effective data visualisation

When data is presented to us in a spreadsheet or in it’s raw form, it can be hard to draw quick conclusions without spending time and patience on a deepdive into the numbers to understand results. However, when information is presented to us visually, we can quickly see trends and outliers. 

A visual representation of data allows us to internalise it, and be able to understand the story that the numbers tell us. This is why data visualisation is important in business – the visual art communicates clearly, grabs our interest quickly, and tells us what we need to know instantly.

In order for data visualisation to work effectively, the data and the visual must work in tandem. Rather than choosing a stimulating visual which fails to convey the right message, or a plain graph which doesn’t show the full extent of the data findings, a balance must be found. 

Every data analysis is unique, and so a one-size-fits-all approach doesn’t work for data visualisation. Choosing the right visual method to communicate a particular dataset is important.

Choosing the right data visualisation method

There are many different types of data visualisation methods. So, there is something to suit every type of data. While your knowledge of some of these methods may span back to your school days, there may be some which you are yet to encounter.

There are also many different data visualisation tools available, with free options available on Google Charts and the open sourced Tableau Public.

Examples of data visualisation methods:

  • Charts: data is represented by symbols – such as bars in a bar chart, lines in a line chart, or slices in a pie chart. 
  • Tables: data is held in a table format within a database, consisting of columns and rows – this format is seen most commonly in Microsoft Excel sheets.
  • Graphs: diagrams which show the relation between two variable quantities which are measured along two axes (usually x-axis and y-axis) at right angles.
  • Maps: used most often to display location data, advancements in technology mean that maps are often digital and interactive which offers more valuable context of the data.
  • Infographics: a visual representation of information, infographics can include a variety of elements including images, icons, texts and charts which conveys more than one key piece of information quickly and clearly.
  • Dashboards: graphical user interfaces which provide at-a-glance views of key performance indicators relevant to a particular objective or business process.
  • Scatter plots: represents values for two different numerical variables by using dots to indicate values for an individual data point on a graph with a horizontal and vertical axis
  • Bubble charts: an extension of scatter plots which displays three dimensions of data – two values in their dot placement, and a third value through its size.
  • Histograms: a graphical representation which looks similar to a bar graph but condenses large data sets by grouping data points into logical ranges.
  • Heat maps: show the magnitude of a phenomenon as a variation of two colour dimensions which gives cues on how the phenomenon is clustered or varied over physical space.
  • Treemaps: uses nested figures – typically rectangles – to display large amounts of hierarchical data
  • Gantt charts: a type of bar chart which illustrates a project schedule, showing the dependency relationships between activities and current schedule status.

Data visualisation and the Covid-19 pandemic

The Covid-19 outbreak was an unprecedented event which had never been seen in our lifetimes. Because of the scale of the virus, its impacts on our daily lives, and the sudden nature of abrupt change, the way public health messages and evolving information on the situation were communicated was often through data visualisation.

Being able to visually see the effects of Covid-19 enabled us to try to make sense of a situation we weren’t prepared for. 

As Eye Magazine outlines in the article ‘The pandemic that launched a thousand visualisations’: ‘Covid-19 has generated a growth in information design and an opportunity to compare different ways of visualising data’. 

The John Hopkins University (JHU) Covid-19 Dashboard included key statistics alongside a bubble map to indicate the spread of the virus. A diagram from the Imperial College London Covid-19 Response Team was influential in communicating the need to ‘flatten the curve’. Line graphs from the Financial Times created visual representations of how values such as case numbers by country changed from the start of the outbreak to present day. 

On top of this, data scientists within the NHS digital team built their capabilities in data and analytics, business intelligence, and data dashboards quickly to evaluate the rates of shielded patients, e-Referrals, and Covid-19 testing across the UK. 

The use of data visualisation during the pandemic is a case study which will likely hold a place in history. Not only did these visualisations capture new data as it emerged and translate it for the rest of the world, they will also live on as scientists continue to make sense of the outbreak and the prevention of it happening again.

Make your mark with data visualisation

If you have ambitions to become a data analyst who could play an important role in influencing decision making within a business, an online MSc Computer Science with Data Analytics will give you the skills you need to take a step into this exciting industry.

This University of York Masters programme is studied part-time around your current commitments, and you’ll gain the knowledge you need to succeed. Skilled data analytics professionals are in high demand as big data continues to boom. With us, we’ll prepare you for a successful future.