Couldn’t make it to Catalyst’s SEMPO breakfast event? No problem! Today we’re recapping Paul Shapiro’s very helpful data visualization session. We’ll summarize his actionable tips for using compelling visuals to illuminate data insights and trends.
What is Data Visualization and Why Should You Care?
Shapiro began his session by defining data visualization. For marketers, data visualization refers to visual representations of abstract data. In other words, data visualization helps our brains process and understand information.
So as a marketer, why should you care about data visualization? Is it really worth the time and effort? The answer is a resounding YES. As mentioned above, data visualization isn’t just a pretty way to present numbers, it actually helps the human brain make better sense of that data, and can help you uncover insights that you might not have noticed otherwise. Our brains are also really good at remembering pictures, so visualizing information can help us remember information much more effectively than if we only hear information or see a spreadsheet full of numbers.
The Two Types of Data Visualization
Shapiro explained that there are two main types of data visualization. If you’re trying to unlock the story behind your data, using data visualization for discovery can reveal what all those digits are trying to say. Visualizations created for discovery will typically show more data initially, and then can be analyzed and refined using Schneiderman’s Mantra:
- Zoom and Filter
- Details On Demand
For example, initially you may start off by looking at a full set of data, like trends in searches for specific topics over a one-year period. Then, you can drill down further where you notice trends, for example by shortening the length of time you’re looking at and filtering the lists of topics or data points. And finally, you can highlight details in your visual to help them stand out, for example through labeling or otherwise highlighting key data points.
If you already know the story you’re trying to tell with your data, you’ll want to take a different approach. In this case, the visual that tells the best story will depend on what type of data you’re dealing with.
Some examples of data types visualizations can help with:
- Comparison: Compare magnitudes
- Relationship: Show correlations, outliers, and clusters
- Distribution: How values are distributed along an axis
- Composition: How parts of a whole related to each other
Creating the Optimal Visualization
Shapiro explained that many people stop after their first attempt at creating a visualization, even if it might not be ideal. So once you make a first attempt at creating a visualization, don’t be afraid to try out multiple kinds of visuals to represent the same data set. If your first attempt seems like it might not be ideal, you may find a different type of visualization shows the story your data is telling much more effectively. And once you settle on the type of visualization you want to use, don’t forget to revise it using pre-attentive attributes (like color and size) to make it even more compelling.
Other Data Visualization Rules & Tips
What are some rules of thumb for data visualization? Shapiro provided the below tips and tricks for the most impactful visuals:
Don’t Clutter Up Your Visualization: Avoid any unnecessary lines, colors, pattern fills, and chart data, essentially anything that isn’t necessary to help tell your data’s story.
Use a Color Blind Friendly Palate: 8% of men and 0.5% of women are color blind. Use a color palate that works for everyone, and remember that someone in your audience may not be able to fully understand your visualization if you don’t.
Show Upward Sloping Graphs When Possible: Research has demonstrated that upward sloping graphs are perceived more positively.
Use the Right Data Scale: When creating bar charts, start your scale at zero and end a little above the highest value with a round number.
Choose Chart Types Appropriately: The type of visual you use depends on your story and the type of data being analyzed. For example, bar charts are great for emphasizing and comparing individual values, while line graphs are helpful for analyzing patterns and exceptions, and heat maps are helpful for analyzing cyclical patterns and exceptions.
Remember Specific Visualizations Helpful for Search: Network graphs are great for visualizing site architecture, Venn diagrams can help visualize overlapping keywords between clients and competitors, word clouds help visualize social-keyword research, and bubble charts help visualize keyword volume and competition by theme.
Use the Cleveland & McGill Hierarchy: This will help your visualization be as accurate as possible.
Creating the ideal data visualization isn’t quick or easy. However, leveraging these basic data visualization principles and Shapiro’s helpful tips can help you create better, more compelling visuals to drive insights and actions.
Be sure to check out the full deck: