Let your data shine

17 November 2017

The value of good data visualisation

So you’ve conducted your research, and collected a whole heap of data, now what?

Data can give you great insights into your users’ needs and experience. But it’s how you visualise this information that has a huge impact on how others interpret your findings.

Why you should care

Good data visualisation tells a story. Before you start, make sure you figure out what your audience needs to know. They don’t need to know everything, so keep it simple and give them the key findings.

Data visualisation is a great tool for communicating critical information to everyone from executives to users. It makes it easy for people to read and understand complex information. The amount of time and effort needed to make sense of it is reduced. A bar chart, for example, communicates findings better than a complex table.

An illustration showing data on pet ownership in table format

An illustration showing data on pet ownership in graph format.

It is easier to see the trends at a glance when the data is represented in a graph


Getting it right

Data visualisation isn’t just about pretty graphs and fancy stats. It is about helping the reader understand and obtain the right information.

Paying attention to details can make the difference between a top notch visualisation and failing to get your message across.

Don’t get lost in the data

Large amounts of data can be overwhelming. Just because you’ve got it, doesn’t mean you need to flaunt it. Don’t try to fit it all in one place and plot every data point but condense it into meaningful groupings.

An illustration showing a very detailed graph

An illustration showing a simplified graph with clear groupings of data.

Grouping the pets into categories makes it easier to see which type of pet is the most popular


Keep an eye on your scales

Scales are the intervals in which the data is plotted, for example, using kilograms as opposed to grams. Be sure to use a scale that will accurately highlight the difference between data points, without being misleading.

Missing or inconsistent scales can result in misinterpretation. Scales need to be accurate to give your data context.

An illustration showing graph with the data mapped to two different scales

An illustration showing a graph with the data mapped to one consistent scale.

The two inconsistent scales make it seem like similar numbers of cat and dog owners are walking their pets. Using the one consistent scale makes it clear not many people walk cats.


Don’t forget about your range

The range is the difference between minimum and maximum values in your data set.

It can be difficult to decide whether to start the range at zero. There are times when doing so is necessary to accurately visualise your data but at other times it can be misleading.

Scale and range are closely connected. A good visualisation provides a balance between the two. Choose the right range to make your visualisation effective.

An illustration showing graph with the data mapped with a range not starting at zero

An illustration showing a graph with the data mapped with a range starting at zero

Not starting the range at zero makes a small difference in overall numbers feel larger than it should.


Right graph for the right job

The type of graph you use to represent your data is very important. Some graphs are better suited to represent certain data types than others.

A pie chart without percentages loses its meaning. Line charts for example, are well suited for timelines, while bar charts are better suited for making comparisons.

An illustration showing data in a piechart

An illustration showing a data mapped in a bar graph

Small differences in the data are not always easy to see in a pie chart.


Go forth and visualise

Keeping it simple and applying these tips will help you visually communicate your message with impact. Always remember to:

  • Keep it simple and only present the important information.
  • Choose graph scales that best highlight your findings.
  • Choose appropriate ranges for your graphs so they aren’t misleading.
  • Pick the right graph for your data type.

Data doesn’t have to be boring. Make it appealing and memorable for your audience.

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