Three ways to improve your DataViz
If you’re like me, you think you’ve got a pretty good handle on data visualisation – you know how to make basic customisations to graphs in Excel, you know you should probably think carefully about whether or not to put that large table into a report, and you know that you should think through your colour choices so that you don’t end up with the overused, default palette in most Office software.
Then you go to make a report, and chuck in a graph, and it just looks… lacklustre. Or at worst, downright confusing. Good dataviz tells your reader about important stories that could otherwise be lost in this age of digital consumption and skim reading. So how can you up your dataviz skills to help get these stories out?
1. Learn from existing knowledge
I’m not going to attempt a round up of individual resources or courses as there are a number of fairly comprehensive ones already.
A great list of starting point resources is Visual Cinnamon's Learning Data Visualization. This curated list includes posts about learning data visualization, how experts go about designing their work, and useful best practices to know. There are also links to useful books and tutorials.
Another good place to launch off from is David Venturi's An overview of every Data Visualization course on the internet. Although the title is hyperbole, this roundup does list a large number of courses with a focus on courses that cover data visualisation theory and give learners a thorough overview of data visualisation tools. I'm normally more of a face-to-face learner, however for something like dataviz, I think the online environment has some great benefits. The skills taught can be easily applied to your data, as you learn them, and on your own computer at your own pace.
Two other online learning options from the evaluation field and not included in the above are:
Evergreen Data Academy – run by Stephanie Evergreen (Evergreen Data), who is one of the main contributors to the visualise data area of the Rainbow Framework Rainbow Framework, and has a wealth of knowledge, three books, and a PhD on data visualisation.
2. Explore methods
The above resources are useful for getting a better understanding of the theory behind dataviz and the application of specific tools. But sometimes, it's useful to just get a quick overview of the methods available to you so you can start applying this theory to your specific needs.
The BetterEvaluation Rainbow Framework task, Visualise Data, is a good place to start. It contains methods for:
- Seeing relationships among data points
- Comparing a set of values
- Viewing changes over time
- Seeing the parts of a whole
- Analysing a text
- Seeing the world
I’d also recommend taking a look at Stephanie’s Qualitative Chart Chooser, which has guidance on choosing the best chart for the often tricky job of presenting qualitative data.
Another important part of exploring options though is to look at how they are being applied in the real world. Some great sources of inspiration include the Kantar Information is Beautiful Awards long and shortlists, the #dataviz thread on Twitter, or even a quick Google image search.
Perhaps the most important of these three suggestions: Get your hands metaphorically dirty (or physically dirty, if you're into things like data pottery) and start experimenting. And I don't just mean when you have a report due that needs a graph.
Try pushing yourself further by regularly taking part in a dataviz challenge such as #MakeOverMonday, where each week a chart is posted with its original dataset and participants are asked to do it better.
Or you could try your hand at requests from people looking to get some help visualising their data.The Reddit DataViz Requests is one option for this, or consider joining a Viz for Social Good hackathon to help nonprofits turn their data into useable and beautiful graphics.
Interested in getting some feedback on your work?
You can self-rate your visualisation with the Data Visualization Checklist. Developed by Stephanie Evergreen and Ann K. Emery, with validity testing by Sena Sanjines, this tool allows you to upload a version of your visualisation and rate it according to the checklists criteria.
Alternatively, the r/dataisbeautiful forum on Reddit is a good place to get some feedback on a visualisation you’re working on. They also have a dataviz Battle that has a prompt dataviz challenge each month.
And if you've created something you think is really great, think about entering a dataviz competition, such as the annual Kantar Information is Beautiful Awards, which offers over $20,000 in prizes across a range of subjects.
Let us know how you go
We'd love to know about your dataviz journey. What do you struggle with the most? Are there any other suggestions or specific resources that helped you that you'd add to this list? Have you pushed through and created a piece of dataviz that you'd like to share? Or alternatively, have you created any guidance or resources on improving dataviz that you think would help others? Let us know.
- Rainbow Framework :
'Three ways to improve your DataViz' is referenced in: