7 minute read time
Glenn Exton, Head of Data and Analytics at RBS International, looks at how we can create strong data visualisations to quickly and clearly tell a data story.
The phrase ‘a picture is worth a thousand words’ is especially true when it comes to data visualisation.
Let’s say you’re looking at a graph; your eyes scan back and forth. In this time your brain has processed each shape, positioned them all in relation to each other, cross-referenced with its inner library of memories to give meaning, and reached a conclusion.
The brain has enormous capacity for computation, especially when it comes to visuals. We can make connections with pictures that we can’t with language alone. When presented with visual data, the brain will fill in gaps, spot familiar patterns and recognise outliers – it’s how we notice when something is out of place, or discern the shapes in an optical illusion. But, like an illusion, visuals can cause our brains problems if they cause us to jump to the wrong conclusions.
The brain also wants simplicity – it craves the eureka moment that comes from decoding information into meaning – and if it doesn’t achieve this, it gets frustrated. When visualisations are designed badly, they deprive the brain of the pleasure of really understanding a data set.
So why not use data to convey more than words? Let’s look at how best to visualise data to inform the viewer and help them take action.
Thinking about data visualisation
What’s confusing in a written body of text can quickly and easily be understood if the same information is presented in a table or chart. This is the principle behind data visualisation.
There are three stages that enable the viewer to truly understand a visualisation. The first is perceiving the data, which involves decoding the representations of data and converting them into perceived values. The next stage is interpretation, i.e turning perceived values into meaning. The final stage, comprehension, requires the viewer to consider what all this information means for them personally. While you can influence each of these stages, ultimately, what conclusion the viewer will come to depends on their own preferences.
Communication doesn’t fail because the data is inherently confusing or because the tools we’re using aren’t up to scratch, but because of a failure to put the user first
It can be all too easy to fall into the trap of trying to make your visualisations beautiful, or to cram in all the variables you think might be useful, and in the process fail to get across the true value of the information. In these cases, communication doesn’t fail because the data is inherently confusing or because the tools we’re using aren’t up to scratch, but because of a failure to put the user first.
When thinking of how to visualise your data, regardless of whether you’re designing the sleekest business intelligence dashboard or revolutionising how customers could view the details of their bank account, you should always start by asking: who is my user, and what information do they need to help them make an informed decision?
Following the tips below should help you stay user-centred when thinking about data visualisation.
1. Know your audience
Taking a couple of minutes to put yourself in the shoes of the person who will be using your data can unlock critical insight with regard to how you should present it. If your user is a critical thinker they’re probably comfortable with a bar chart but may be put off by multiple vectors. Equally, if your data is for a team of innovators you may need to use pictorial representations instead.
2. Know the questions your audience needs answers to
Before getting into the fun stuff of seeing what visuals you can create, determine what questions or insights your audience is looking for answers to.
3. Know where data originates from
Creators may be so impressed with a data visualisation that they may not step back and wonder where the data used has come from. Take time to look at your data sources and ask yourself: can they be trusted? How accurate is the data? Has permission been granted to use it? And was it ethically sourced?
4. Utility over beauty
The first pitfall of data visualisation is the desire for beauty at the cost of communication and making it inclusive. Your visualisation is there to communicate, inform and challenge, so if the aesthetics are getting in the way of what your user needs to know, and their ability to use the data, you should rethink your strategy. Always ask yourself why: why are you using that colour? Why are you arranging your graphs in that way? Am i excluding users from seeing and interpreting the insights I'm seeking to convey?
5. Less is more
Wanting to show all of the amazing data you’ve gathered is understandable, but doing so can end up confusing the end user. Even if your chart looks impressive, using too much data can obscure patterns and make a visualisation difficult to navigate.
6. Try, test, ask
The best way to know if your data is communicating what you want it to is simply to ask – and you should do so right at the beginning of the process. Before committing to one visualisation technique, get your colleagues and stakeholders to have a look at a draft, and if it’s not working, try a few different approaches and ask again. You could sketch the data visualisation first and practise putting data storytelling techniques to good use. Undertake user research and hold sessions with potential users and gather feedback early and often.
How do I decide which visual to use?
When you create visualisations, there are so many different graphics out there that it can be difficult to decide on which one to use. In addition to the age-old pie chart and bar graph, there are increasingly more innovative ways of presenting data, from alluvial diagrams to choropleth maps and beyond. Data Viz Project https://datavizproject.com/ has useful examples of these and further visualisation models.
In order to create meaning in your data visualisation, ask yourself what type of data you have. Is it:
- based on categories
- based on maps
- best represented as a network
- a sequence of data generated over time?
The options in front of you are immense; the challenge you face is how to convey a visual story to your viewer that will help them make an informed decision on what action to take. To help you explore what types of data visualisations that might work for your project, you can find a rich set of information at From Data to Viz https://www.data-to-viz.com
Telling a story with your data
Once you have honed your data visualisations, you need to consider how they will operate as a whole, as well as individually.
Data storytelling capitalises on our brains’ inherent capacity for narrative association by combining different data sets and visualisations to construct a narrative for the audience to navigate and extract information from.
Allowing for the comparison of many different types of visualisation also increases the opportunity for new connections to be made and opens the door to fresh thinking. You are essentially forming an environment in which the brain can search for and find its own eureka moments of connection, and making the user an active participant in the data story – which is the ultimate goal.
Data visualisation is a powerful tool, but, as they say, with great power comes great responsibility. It is your responsibility to ensure that your data is fit for the person who is going to use it, that it clearly communicates what it needs to and that it helps them to make informed decisions. By keeping in mind user-centric design and the principles of data storytelling, you can contribute to a data-rich culture that both informs and empowers users.