An Introduction to Data Visualisation

Living in the Information Age we have such a vast and ever increasing amount of data around, that it is often at times very overwhelming.  Using data visualisation techniques, you can make sense of all this data and communicate it easily to others.

So what exactly is data visualisation?

Data Visualization (or dataviz) is the creation and practice of the visual representation of data. It is used to give visual form to numbers and other abstract concepts, with the function of re-presenting that information in order to communicate it more effectively. A spreadsheet full of numbers is harder to read, make comparisons or detect patterns from than a graph is, therefore a graph is far more effective way to communicate that information.

Once predominately used in the fields of maths and science, data visualisation has exploded in popularity in the past few years, as the advancement of information technologies has improved our ability to record information.

So you have some data?

“Data” and “Information” are synonymous, so the range of different kinds of data is broad, but when referring to “data”, most people generally think of a spreadsheet numbers, even though this isn’t always the case. Here’s the basic anatomy of a table of data:

So you have some data, well how should you present it? One way would be to look at the type of data you have. Is it over a time period or categorical? Is the data in percentages or is it linked geographically? The context and format of the data is important in selecting a compatible dataviz method but also you decide on a dataviz method based on what you want to show or find with the data.

Examples of data visualisation

In order to understand ways you can visualise certain types of data, here are five examples of commonly used data visualisation techniques and their functions:

Bar graphs: used to show comparisons between non-ongoing, categorised data.

Line graphs: used to show values over a continuous interval or timespan in-order to show trends and relationships.

Pie charts: give the reader a quick view of the proportional distribution of categorised data.

Scatterplots: used to show correlations/patterns between two variables.

Choropleth maps: provides a way to visualise values over a geographical area in-order to show variations or patterns.

Info-graphics and data visualisation

People often confuse and call pieces of data visualisation as an info-graphic. While there is some overlap, they are in-fact two different ways of displaying information.

A piece of data visualisation is usually just a chart on its own that lets the data speak for itself, without context or editing. The primary goal here is to make sense of the data.

An info-graphic however, has to tell a specific story to an intended audience and often uses statistics or piece of data visualisation to do so. Because of this, info-graphics are subjective and have some kind of deliberate influence behind them. Also, info-graphics are more illustration and graphic design heavy.

So if you have data you want to communicate, first you need to check who is your intended audience or where are you going to be displaying it. If you’re only looking to make of sense of a table of data and need it to be displayed as objectively as possible, then data visualisation is ideal. On the other hand, if you’re planning to explain your data to a wider audience, tell a news story or need a marketing tool for over the internet, then an info-graphic is a better option.