Take a look at these two graphs of fruit sales:
A recently published study from Harvard Business Review looked at how the color choices impacted how people analyzed the data. They pose the following questions about the data in the charts:
Which fruit had higher sales: blueberries or tangerines? How about peaches versus apples? Which chart do you find easier to read? Continue reading Strategic color choice to improve your graphs
Thanks to Hub member Lisa Mwaikambo for sharing the upcoming TechChange Social Media for Social Change course that has some specific focus in data visualization.
Continue reading Why Analytics and Data Visualization Tools Matter
One of the key steps to creating effective data visualizations is testing them out, seeking feedback, refining and adapting your message to make sure that it reaches the right people with the right tone. When I saw this article focused on feedback by Katie Dill, Airbnb’s Head of Experience Design, I was immediately drawn into it because of steps she presents for effectively seeking and reacting to critique, especially #4. Make it tangible Katie says.
People can’t visualize everything you’re showing them at the same time unless they can literally see it all at the same time. Allowing them to interact with your content and giving them the space and visibility to do so will only improve your results.
Continue reading The dreaded feedback – Tips and tricks
A common debate in data viz is whether or not graphics can, and should, stand on their own. While there are occasions when you you want your audience to truly draw their own conclusions, the authors of a data viz should usually have a clear story that they are trying to tell. So why not tell that story, in words, and use clear charts to illustrate the story? Or think about it the other way around: accent your graphs with clear text to make sure the reader is interpreting the data the way you intend.
Bloomberg.com features some great storytelling through data visualization that illustrates this point. Their story on “How Americans Die” walks the reader through what could be a very complex data story.
They even show us how they found their headlines: by exploring data and noticing trends that looked unusual.
And then they show the data that explain the strange trend.
It’s as though they’ve walked the audience through an academic journal article without the small type, illegible black and white charts, and highly technical details only a handful of readers will understand. This is how you get your data to a large audience!
While this example is an interactive online platform, I can also imagine ways to do this in a more simple print document.
What do you think? Are these series of graphs more digestible when paired with the easy-to-read text story?
We’ve gotten great feedback from many of you about how useful the choose-your-chart-adventure flowchart has been, and wanted to point out an evolved tool available from Juice Analytics.
The tool is designed to help you choose your chart type, and to provide Excel and Powerpoint templates to make it quick and easy to create and customize charts and graphs with your own custom data.
There’s nothing I love more than simple tools that demystrify data viz. If you find the tool useful or have a great example from using it, let us know in the comments below!
Fast Company is one of my go-to sites for design ideas and resources. They routinely share great examples of data visualizations and informational graphics, and do round ups of tools and tricks like the one featured today.
The great thing about many freely available charting and graphing tools are how simple and easy they are to use. Gone are the days when only a skilled coding genius can generate web-friendly graphs and charts. Now, tools for great design are freely available. Remember to be cautious when playing with these new toys though: often, when you use an open source tool, publishing you visualization means making the data behind it public as well. If you’re working with sensitive program or donor data, that may be a reason to take pause.
Check out the list of 30 tools, and share any additions you have for the list or experiences using the different tools in the comments below.
One of the key parts of building a great visualization is finding the story in your data: what is your point of view? What should your reader take away at a glance and with further scrutiny?
There are a number of design tips and tricks to help a viz capture that story, but the Eager Eyes blog (a highly recommended resource in itself!) posted a different perspective on storytelling in visualization. Robert Kosara, the author, pens a short and elegant summary of stories their role in visualization, seeing visualization as a path or guide through a dataset.
Give it a read, and leave your thoughts in the comments below!