10 November 2022
When it comes to visualizing data – the goal isn’t just to present something that looks nice. The purpose of taking the time to create a beautiful, compelling data dashboard is about communication, and – ultimately – storytelling.
When done well, a thoughtfully prepared dashboard – like those created using Qlik + Vizlib – can take an end user or audience member on a journey, while educating, engaging, or even entertaining them along the way.
And truly exceptional data visualizations – like the one we’re exploring in this blog, below – can do all three at once.
This data dashboard investigates and unravels the data behind Nobel Prize winners between 1901 and 2020. Using an array of sophisticated data visualization techniques, the developer-creator paints a clear picture of several key metrics related to Nobel Prize winners, including gender, subject area, and country.
Of course, it takes time and practice to cultivate the skills required to create something that looks and feels interactive, relevant, and exciting – but there are a few tips, tricks, and tools that you can use now to begin creating dashboards like the one you see here.
The data dashboard explored in this blog was created as part of a data visualization challenge. So, it was built to be over-the-top beautiful (and it is!), but that doesn’t mean it can’t teach us valuable, practical lessons about visualization techniques and design.
In this article, we’ll look into these techniques one at a time, explaining how the visualization was created, why the technique is right for the job, and how Vizlib made it possible to transform this data into a striking, interesting story.
Theming is an important part of building an impressive and engaging data visualization. This data dashboard uses Vizlib Sheet Menu for theming, which makes it easy to customise and adapt to your needs (especially since you can add CSS for even more flexibility).
Another important part of creating a dashboard that’s interesting is choosing how to present the data you’ve visualized. Said otherwise: choosing which data visualization technique to use is just as important as choosing where to display it on your dashboard. A dashboard should present the maximum amount of information in the least congested or chaotic way possible, so choosing how to arrange information – and having the freedom and flexibility of a solution designed for customisation, like Vizlib – is critical for success.
You’ll notice that the dashboard we are looking at here uses a limited number of colours, and that those colours are relatively muted and neutral. This is a conscious choice by the creator to let the data do most of the talking without distraction. It’s also consistent with the data set being presented, which is historical, non-fiction, and scholarly.
Alternatively, if you were examining a data set about the use of fireworks during festivals over time, you might choose to use more striking colours with more variation. Ultimately, it’s important to create an app that feels representative of the data.
Generally, developers and creators have their own processes for deciding how to present their visualizations: you may create an outline first, or you may find the perfect balance through trial and error. However you go about it, striking the right composition – balance between text and images, contrast between light and dark, harmony between portrait and landscape elements – is an undeniably important part of making sure you get your message across clearly and effectively.
The example we’re exploring today does a great job of presenting the data at hand in an attractive, appealing manner. Let’s dive in and see exactly how it was built.
Here, we see a detailed breakdown of Nobel Prize winners by subject area. The data displayed includes the number of Laureates (winners) from each subject, broken down further by gender and average age. The oldest and youngest winners in each area are also clearly shown.
The data visualization technique used to display this data is Vizlib Tiles. Tiles are an excellent choice for this particular data visualization because it allows a creator to repeat consistent data across each tile while also using images (something that’s not possible with a Table). This is an important distinction, as the use of images here to show the oldest and youngest winners is particularly effective because it clearly shows contrast, and lends a human dimension to the content being shown.
Some notable takeaways from this visualization include the difference in age between the oldest and youngest-ever winners: 80 years! Also interesting to note is the average age of winners, by subject area: the lowest average age for winners is in the area of Physics (56.2), compared to 60.9 years of age for winners of the Nobel Prize for Peace. This is particularly interesting considering that the Nobel Prize for Peace is the only subject that requires no specialised area of work or credentials.
With so much information presented in such a clear way through the use of Tiles, it’s easy to pull out interesting bits of information and begin to paint a clearer picture of a data set that spans over 100 years!
Above, we see another unique breakdown of the Laureate’s age over time and separated by subject matter. The visualization technique used here is a Vizlib Library Scatterplot, which allows a creator to visualize trends with two measures and one dimension.
This visualization technique is a strong way to present this data because it allows you to dive deeper with regression analysis (the red line), which is a clear way to show trends. It also allows for the addition of images in the background (as shown here with the subject area symbols), the customisation of the minimum and maximum measures, and for the annotation of important measures (shown by the vertical dotted line).
Interesting to note from the above Scatterplot is the speed with which the average age of Laureates is changing across the six subject areas. For example, while Laureates in both the Literature and Peace subject areas are getting younger, they’re doing so at totally different rates. With such an easily digestible visualization, it’s easy to draw inferences and begin meaningful conversations with this data.
Here, we see a detailed visualization of Laureates by year, category, and gender. The visualization technique used is once again a Scatterplot – but this one is full of surprises. . .
In order to create the distinct look of this chart, the creator implemented a technique typically used to highlight a certain area with colour, to do something else: create labels! The white labels that you can see on the far left of the visualization – the ones showing the categories – aren’t labels at all! They’re actually “reference areas,” with text overlaid. To tell an even more visually compelling story, the creator added icons next to the labels for added context.
This particular data visualization is compelling because of how straightforwardly it shows a few key things about this data set: first, you can see right away that there were no Nobel Prizes awarded for Economics until 1969. When put into the larger context of the overall dashboard, it becomes instantly clear why there are far fewer total Nobel Prizes in Economics than the other categories. Also, this chart clearly shows the breakdown of male versus female winners through the use of coloured squares. With information so clearly presented, it becomes even easier to make sense of the data being presented.
Here, we see a detailed breakdown of Nobel Laureates by country and category, and also by university and category. The data shows the concentration of these measures through the use of custom colours.
The visualization technique used to display this information is the Vizlib Grid Chart. Using the Grid Chart allows the creator to clearly show two dimensions and one measure, and it allows for total customisation with colour and shape.
Some notable takeaways from this Grid Chart include the high concentration of Laureates from the US (274), which is much higher than the next-closest country, the UK, which has 103. Even more interesting is the fact that the US, with such a high number of total winners, only has nine total Laureates in Literature, whereas France – which has only 20% as many total winners as the US – has 81% as many Laureates in the area of Literature.
Once again, extrapolating and analysing this data is made remarkably easy and approachable by the visualization technique used, which presents this information in a straightforward and easy to digest manner.
The trick to actually mastering these techniques and creating a killer data dashboard is simply to get started! Since that’s easier said than done, it helps to break down the creation process into steps, which you can tackle one-by-one so you can start supercharging sooner, not later:
Building an incredible, engaging dashboard requires ingenuity, skill, and out-of-the-box thinking. And for a lot of users, designing something as comprehensive and aesthetically pleasing as what we’ve explored in this piece isn’t front of mind.
But with a few tips, tricks, and tools from Vizlib (and a bit of practice!), any creator can take their skills to the next level and begin building dashboards that go beyond visualization to tell a clear, compelling, and convincing story. Explore more incredible apps here.
Data set used: https://www.kaggle.com/datasets/imdevskp/nobel-prize
Creator: Jochem Zwienenberg, Victa BV.
Vizlib’s feature-rich data visualisation products will help you get started on your data storytelling journey.
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