BIG QUESTION: WHY IS DEMOCRATISING DATA SIGNIFICANT TO THE WORLD WE LIVE IN?
We are constantly being fed information. From social media, to ads on the train, to news report notifications. A lot of the time, we don’t even know what we’re looking at. How can anyone make informed decisions if they are unable to understand and learn from all of this information? Visualising data helps us cut through the noise and gives the audiences information in a clear and concise way. Breaking down complex data into palatable graphics allows people to digest, understand and make their own informed decisions about the information they are being presented.
Imagery is a universal language, that can make information accessible for almost everyone. It allows us to not only show the impact of our story, but also highlight trends, compare outcomes, find patterns, identify missing information, fill in those gaps and so much more.
Knowledge is power.
FUN FACT: Encoding information into a visual language means that it can be rapidly and intuitively decoded – ‘The human brain processes images 60,000 times faster than text.’
So how do we democratise data? With GOOD Design
A good design starts with asking the right questions to figure out what value this piece of data may give the audience. I care about this thing, but why should you?
OUR 3 TOP TIPS FOR GOOD DATA VISUALISATION DESIGN…
1. KNOW YOUR AUDIENCE
Thinking about the audience is key to creating well designed data for them. Where do they live? What platforms are they on? How can we connect with them? Do they respond to emotions or facts? Should we lead them towards the data, or make it available to them where they are?
Mapping out the audience behaviour allows us to figure out how to reach them most effectively. This includes understanding the best way to connect with them, and which elements of the information are most important to them – vs what we should leave out.
2. ACCOUNT FOR ACCESSIBILITY
We need to make sure that the data visualisation is actually accessible to the target audience. This means considering language justice, design for neurodiversity, learning styles and physical/mental accessibility. Are we creating data visualisations that people can understand, process and then remember?
3. CONTENT & CONTEXT
Figure out what we want to achieve and what we want the audience to take away before diving into the design. The goal is to tell them a story with the data content in a context that they can understand and relate to.
And if that wasn’t enough, we’ve added in a few extra…
- TITLES: try to summarise the key message in the chart title. Even better if there’s a limit of 10 words!
- WHITE SPACE: don’t be afraid of using white space in the visualisation. A bit of breathing room helps identify and break up pieces of data.
- UNICORN PUKE: don’t over use bright, bold colours when creating the visualisation. This can overwhelm the user and cause them to miss the key message of the design. Basically, don’t make unicorns sick!
- EVERYTHING EVERYWHERE ALL AT ONCE: avoid trying to include everything in one data visualisation. It can be too much and the main message will be lost. Instead, start with the question ‘what is the value of this data visualisation’ and take it from there.
- Moves to impress: animating graphics can add a slick feeling to data visualisation. But animated text is (usually!) a no no. It can easily trigger vestibular disorders and is just not easy to follow.
SO WHAT ARE THE BIG TAKEAWAYS?
Telling stories through data visualisation can be a fun challenge for designers. But are we responsible for making sure the designs communicate it as accurately as possible? Do we have a moral obligation to present data honestly, without skewing people’s views on the information? And is good design really enough to democratise data for everyone?
Raising these questions makes us wonder if it’s even possible to be unconditionally unbiased when it comes to visualising data. According to A Reader on Data Visualization, “it is essential to understand that data and visualisation are not ethically neutral. Data is not unbiased; it is always collected or processed by someone, for some aim.”
Even though data is seen as a trustworthy and scientific fact, it is not always the case. ‘Fake news’ is common, and can be dangerous when followed blindly. It spreads misinformation, which is bad news when people use it to their advantage. For example, Covid numbers were exponentially skewed in China through the way data was presented in order to make the world believe that the problem was solved there. Same thing with the migrant workers’ death toll in Qatar during the last World Cup. Not having a moral compass when dealing with data visualisation is a slippery slope towards deliberate misinformation and deception. I know what you’re thinking: how can we stop that? Stay tuned for part two: How Data Deceives the World.