Data Visualization is a way to show data in an image of some variety
This could include things, such as pie charts, or bar graphs, or other types of plotted data
This could also include things like infographics, which are very popular online
We can even consider animations a type of data visualization
The goal is to present data in an easy to understand format without losing the important points
Most people prefer some variety of data visualization to raw data because they are easier to understand
Why we want to use data visualization
Data visualizations, if done well, can communicate our data to others in ways they can understand faster and better
Just showing raw data to most people is going to be tough because it's hard to see the patterns at a glace
Showing the patterns can help us illustrate our data analysis work or just data in ways lists of numbers can't
There are several ways to show data including charts, graphs, plots, but it's important to know what you're picking and why to make it a good visualization
One expert in the field, Edward Tufte, has come up with criteria, we'll use his because he's considered one of the leading authorities on data visualization at this time
Excellence - Try to make your visualization offer the most bang for your buck. Don't make it more complex than it needs to be. “the greatest number of ideas, in the shortest time, using the least amount of ink, in the smallest space."
Integrity - You need accurate data that is clearly labelled and not ambiguous. Don't try to mislead people with your visualizations
Maximizing the Data-Ink Ratio - Pay attention to what is required vs what is used, don't add things to add things, everything should be valuable on your visualizations
Aesthetic Elegance - Simplicity can be more powerful then clutter, complexity isn't always required
Visualizations that mislead the viewer, either on purpose or by accident
Hiding relevant data, or inaccurately representing data by changing things like scale and proportion, where the chart starts/ends are usually trying to falsely lead you somewhere
Showing too much data to confuse the viewer either obviously like a lot of 3D graphs, or more subtly to give the impression of well thought out analysis when in reality it's trying to hide things
Lack of context, labels, or any way to tell what the visualization is about and why it was made
Using the right data, but in confusing ways to try and lead the viewer into thinking you're saying one thing but you really mean another
Why people might choose a bad visualization on purpose
Manipulation, you can make your data show different things depending on what your aim is.
Not able to tell it's a bad visualization
Trying to say something is a bigger deal then it is, such as cherry-picking data or changing proportions to make it seem like more is happening then really is
Politics and other controversial topics, people can do a lot to attempt others to their way of thinking
Making sure everyone has the same ability to understand and engage with materials
This can be for data visualizations, UI/UX or even just the world around us
This can be a tough thing to do because there can be a wide variety of things that keeps something inaccessible. Some questions we can ask are:
To whom is it accessible
Under what conditions?
For which tasks?
Data visualizations and accessibility
Data visualizations can be tough because of everything from labelling issues, to colour or colour contrast, to lack of alt text
Accessibility should be baked in to what you're doing, not seen as an after thought
Keeping your visualizations simple can help because they can be easier to describe and offer alternates for
Being mindful of colours and contrast is helpful for both text and images. If someone can't see colours, does your visualization still convey your meaning? If not, can you change it so it does?
Think about offering different formats so that it's easier for everyone to understand what you're trying to share