All data that has been gathered at some point of its’ existence has a purpose. A purpose – to be used and shared. Nowadays, it became trendy to visualise data and present it to the public as it brings appealing, but at the same time complex, information to a wide audience. We must admit that data presented inside a infographic is rather more attractive that plain and well-known (but usually found boring) tables with plain numbers. But it’s not only about the outside that matters – inside counts. In this case – the point of visualised data, the story behind it, the message it brings. Alberto Cairo, who is an expert in information graphics and visualisation, identifies four key components of visualisation. Visualisation has to be 1) functional, 2) beautiful, 3) insightful and, finally, 4) enlightening. Having these features in mind we can explore what makes your data visualisation – meaningful.
1) Collected data has a purpose.
It should not be all about nice pictures and pretty images, the visualisation of particular data should disclose an issue, educate or share new ideas (not visualising what is already know in the text). It needs to have a specific purpose. If visualised data holds a message, explains a topic or supports a story – it becomes valuable. It means – it is needed (this is also an important question to to start with – do we really need it?), without it – is there really a point to make it and share it? Some data is often visualised as additional element to the text and often is not needed. If we visualisations they need to say more instead of repeating the text.
2) Data without a context is useless.
Visualised data has to fit into a story or context. Just plain numbers provided in a nice image, can not disclose it all, therefore, context is needed which can richen information with additional information or explanation. Visualised data complements the narrative or go vice versa – bring up deeper understand about the topic through data. We can assume that text and data goes nicely together so why not to try that, where you can explain even more. And often data without clear context can lead to misleading understanding about specific issue and a journalistic does not really want that.
3) It needs to be understandable.
Misleading visualisations gets your audience annoyed, confused and data looses its’ purpose. If a reader can not easily read provided data it makes the whole visualisation process – useless. Well done visualised data should be easy to read, reader should find cues (e.g. additional text, colours, headers, arrows, etc.) which would indicate: how to follow it, where to start, how to read, what’s important, what does it show? Additional guidance helps reader to get around. This kind of data is functional, it enables a reader to use it, gain information. If is understandable it is also – useful, it means a reader can extract information at any time, therefore, it serves its’ purpose. It also depends on our audience, different types of readers are eager to use different kind of data, therefore, it is important to set some guidances when visualising it as we keep our audience in mind (different topics, spheres, age groups, social statuses, etc.).
Finally visualised data needs to be beautiful and fun (e.g. interactive data). Appealing data might catch reader’s attention and this way educate him or her on specific topic. Fun visualisations might lead audience to spend more quality time on it, taking deeper analysis of it and this way reaching the goal of being meaningful. Of course, making visualisations appealing should not take over the main purpose and functionality of it but it can help to improve and boost the effect.
Case 1: Language communities of Twitter. World map.
This is a case of visualised data – map. Language communities of Twitter around the world. When we look at this colourful map, we can state – it is quite nice and appealing but what does it mean? What does it state, only the languages used or something more? Specific year? Factors? In which context we should read it? It is also seem quite complex and hard to get around. Therefore, it serves an example of a case which can be improved. Keeping a eye on those 4 points above we could start from 1) why was is made, what does it want us to disclose? 2) what is the context of this map? 3) where to start, any additional guidance how to read it (numbers, agendas, etc.)? 4) it is fun already to read, visual part could be lead to everyone’s taste depending on the point 3. Solving and answering these questions could improve the value of this visualisation as for now it does not bring a big meaning.
Case 2: The Great War (1914-2014) (press here)
This interactive map is made to celebrate centenary of the great war. This visualisation shows historical information that might be known to some of us, but in a new – interactive way. It states the number of deaths during various wars. It starts with the introduction – what is a Great War, years of it and number of deaths. Deeper exploration is followed giving in comparison other wars and number of deaths per war. Every flower (example bellow) represents an event. What is great about this visualisation that different continents can be filtered. Furthermore, by pressing every flower, we get additional information about the war, its’ duration, participating countries, location and, most importantly, source of the data. This is a great example of an interactive tool which has a context, educates the audience and is made in a very easy to understand and appealing way. Of course, this could not serve as quick tool to access information on the spot but it serves perfectly to educate and remind audience about important matters.
We can see the visualisations needs to be more than attractive if we want to reach our audience. It is important to know what it states for and why it is made in the first place. Making it easy to read and get information from, we can provide data that can be read by anyone. And that is the reason to make it – that people could access it, understand it and, even, share it. This makes it meaningful.
Data Visualization: Making Big Data Approachable and Valuable. Market Pulse. Retrieved from here.
Doing Journalism with Data: First Steps, Skills and Tools. Canvas Network course. Module 5
Essential Guide. Advance data visualization guide: How visualizing data can boost BI. TechTarget. Retrieved from here.
How can we make data more meaningful? Usman Haque, 2014. The Guardian. Retrieved from here.
Tell a meaningful story with data. Daniel Waisberg, 2014. The Rundown. Retrieved from here.