Why we need more data art
By: Matthias Stahl
- We start by looking at the worldwide child mortality table for all countries by year.
- Taking this dataset into excel and creating a generic chart out of it, we can get the visualization we need, but these charts are "lonely" since not a lot of people are compelled to look at them. That data is not conveyed in a way that appeals to wider audiences.
- As a DataViz engineer you have many mediums you can convey your message.
-
The Dataset is at the center of DataViz. But the data is not enough, you must also consider the story the data can tell
-
A process to visualizing data can take the following form:
- Assesing - Asking questions about the dataset itself, and the audience that will consume it.
- Planning - Define a concept: find your message and structure it, Find some inspiration for visualizations and then sketch some ideas.
- Building - Choose tools to create and iterate over your design, this is where you start coding with frameworks such as svelte and d3.js.
- Publishing - Publish the data and collect feedback
- While structuring the story around the data can make the visualization appealing. there's still one piece missing, which is the emotions this data can evoke.
- To truly understand and make the audience to connect with the data we must try to convey the emotions behind the data.
- As humans we are not great at connecting with graphs and charts, due to psychic numbing.
- In contrast images bring emotions to the surface
- Data art can bring emotions to our visualizations in order to convey emotions.
- When your audience doesn't really want to play around with the diagrams, we can use "Scrollytelling" techniques to animate our data art and visualizations.