This is the final installment of a 3 part series I’ve put together to help you improve your team’s data literacy. You can start with “Visual Data Literacy: 3 Fundamentals for Content Marketers” and then go to the 2nd piece, “How to Get Started With Data Storytelling [6 Actionable Steps]” to get a good feel for what I’ve learned over the past several years working with our Column Five and Visage teams to help brands tell important stories using data and other complex information. Feel free to reach out to me anytime at jason at visage dot co to explore ways you can bring your data to life in your content marketing.
Finding a unique story in your data—and bringing it to life—can be a different experience every time you do it.
In some cases, it might be like trying to find a lost contact in a swimming pool full of marbles.
Or it could be like taking a chainsaw to a block of ice to carve out a compelling Homer Simpson sculpture.
Either way, questions remain . . .
How do you find that lost contact—that useful portion in an overwhelming sea?
How do you remove the unnecessary clutter—so your story reveals itself?
Let’s look at how you can bring your data story to life in six simple (and practical) steps.
Step 1: Two paths for finding and visualizing interesting data
There are two paths you can take when you start thinking about visualizing your data:
- Using data visualization for analysis to find nuggets of insight in existing data sets you have on hand.
- Finding the story in your data first (e.g., the answer/interesting angle is evident in the spreadsheet), then visualizing and designing to make a more powerful, engaging presentation of the information
1) Using data visualization for analysis to find the answer/insight/story
The first path to take is to use data visualization to aid in your analysis. If you have a good, friendly data team to work with (see our ebook on this topic), they will likely be familiar with advanced combinations of analysis + visualization software.
Plotting the data can help you see the outliers (or interesting trends) that are happening. In this way, the visualization process itself helps you see what the story or the insight is.
To give a specific example, your data team might generate a historical line chart or scatter plot on a chunk of data. Then, out of nowhere, it becomes obvious—wow, the number of people caught throwing peanuts in class was quite low in 2014 compared with prior years and 2015.
Now you can start asking questions and looking for other answers about what happened in 2014 that might not have jumped out at you just by looking at a big spreadsheet. This insight then becomes a key part of your story.
2) Knowing what questions you want to answer first, then showing the results
In other cases, you might simply know that you are going to show the answers to a question. For example, you create a survey, and then visualize and present the answers. Or, you conduct a local demographic census and plan to show all results in a presentation.
You then look at the size and shape of your data sets and find the best method for displaying each piece of the information.
Either way, it’s important to understand the strongest points and the most interesting context of the data before you spend a ton of time crafting the actual design.
Step 2: Develop a reliable outline
Believe it or not, we’re still not ready to make this look pretty. Welcome to Outlineville. Tedious? Yes. Maybe you’re even bored (which might be a clue that the story isn’t even worth designing). But your audience will appreciate that you took the time to care about whether or not your story follows a logical path to glory.
Outlining is important because it helps you put together the structure for your data story.
Here, you’re working on giving some basic structure to your narrative.
Think of your outline as the bones in your body; the outline gives you the framework to fill in your details.
As you have your data on hand, you can approach this as writing your ideas out and then looking for opportunities to connect the dots. Think about how one insight relates to another and how they flow together in a logical sequence to start forming a strong foundation for the data story.
During this step, you’re asking yourself, based on the data collection and research you’ve done (or that’s been handed to you):
- What are the main takeaways here?
- What’s interesting, and what information here is going to provide value to my audience?
Step 3: Digging deeper
You’ve built out the initial structure for your story.
Now it’s time to dive into the design, right?
What you want to do is look for opportunities to enhance sections that are even richer than you first thought.
When you determine your most interesting statistics, it’s good to ask yourself, “Can I add more context to help my audience understand what’s meaningful about this?”
Here’s an example.
If you’ve identified through your research that 72 percent of the people who parachute like to eat peanut butter, you might think, “Wow, that’s an interesting stat because we’re a peanut butter company and maybe we can market our product to people who parachute.”
Well, you could ask yourself some other questions about this stat, like:
- Is this number on the rise?
- What were these numbers last month? Last quarter? Last year?
- How far back can we get the data on this?
- What’s the actual trend over time here?
It’s not always about a trend over time—you might be able to compare it with data on another sample group of people. For example:
- How does this compare with how parachute aficionados feel about almond butter and sunflower butter?
- How does this compare with data on how people who skydive and cross-country ski feel about peanut butter?
You’re looking at ways to find a more interesting story and help people understand the context of this data.
Remember: this isn’t about serving your own urge to just get content out the door.
Your goal is to provide genuine value to your audience—so give them the additional context to understand why these statistics are significant or the effort will fall flat. I know from our own experiences in screwing this up.
Step 4: Use visual data to anchor your narrative
As you’re plotting out your story and transitioning from the outline stage to writing out the editorial copy for your final data presentation, you’re going to want to use these visualizations to anchor the narrative.
You’re not just writing about your data findings; you’re using visualizations of the data to give your audience a visual cue to retain the key points and to further the story. Doing this increases your audience’s engagement while helping them retain and understand the information.
You can pick up more tips from the Visage e-book, How to Use Data Visualization to Win Over Your Audience.
Step 5: Tell the truth
One of the benefits and challenges of communicating data in today’s marketplace is that your audience is becoming data literate, which means they also know how to think critically about what they’re viewing.
You can’t leave out portions of the story and distort data without at least somebody holding you accountable. People see through distorted data; it often falls flat or becomes a source of controversy.
To communicate with your target audience, don’t come back and try to retrofit the analysis of your data to support what you already determined was what you hoped would be the story.
A good example of this would be if we were to commission a story on why visual content is more effective than text-based content (or audio content). If we left out answers that didn’t fit the hypothesis we hoped would be correct, then we’d essentially be liars—and that’s going to ruin trust in our brand.
In short: your data story should always be straightforward and told in a truthful way.
Step 6: Follow data visualization best practices
There are times when it seems like infographics or data visualization have been overdone.
That’s because many people have thought, “Hey, I’ll just make this into an infographic, or maybe I’ll turn this into data visualization.”
The fact that data is present is not enough for a good story.
Data needs to be presented for maximum appeal and comprehension.
We also need to follow best practices for presenting information when we have it.
It’s not enough to just throw a bunch of charts together and make them look good.
Your audience needs to understand why this information is being presented to them and why it’s important. It’s ok to add some accompanying text to help people understand, even if some data visualization purists hold that you shouldn’t need it at all if you have an effective visualization. Your goal isn’t to earn praise from Edward Tufte. It is to communicate with your audience, who you hopefully know well.
The temptation is to take a leap straight from the data into designing the content. Urgent editorial deadlines and rush projects are often arbitrary (even if they don’t seem like it at first) and should be avoided like the plague.
This approach results in either lengthy (and expensive) designer revisions or a disjointed story, because you have to make changes in a piecemeal fashion.
So even though it isn’t always as fun or isn’t the most exciting stage of the process, spend the time to structure the data story from the beginning—create a good old-fashioned outline.
And take some extra time to dig deeper into the data itself; it will ultimately lead to a smoother design phase and save you time and money in the long run.
Give yourself some time to refine so you can keep the quality bar high (but not at the level of perfectionism, because then you’ll never publish).
Want to learn more about getting your hands on your company’s hidden valuable data—and turning that data into vivid stories?
Grab our new e-book, The Content Marketers Guide to Data Storytelling, and you’ll learn how to:
- Build a dynamic duo (marketing + data team)
- Ask questions that lead to more interesting answers (data)
- Craft original stories for your audience and differentiate your brand
Thanks for reading!