Visual Data Literacy: 3 Fundamentals for Content Marketers

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This is the 1st of 3 posts I’ve put together to help you improve your team’s data literacy. When you’re done with this one, you can view the next post “How to Get Started With Data Storytelling [6 Actionable Steps]” to put these fundamentals to work. The last post, “How to Bring Your Data Storytelling to Life” helps you get familiar with the process of turning your spreadsheet into a structured content outline and to begin working towards a polished final design to foster engagement with your story. 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. 

In content marketing, many words are written, but literacy is lacking when communicating with data. When data is misrepresented, it can lead to inaccurate communications and problems for a brand.

You may or may not want to become a data scientist, but understanding the fundamentals of visual data literacy is quickly becoming a mandatory skill for modern content marketers. But that’s no problem because, by thinking like an eager-to-learn kindergartener, the subject matter will become less intimidating if you just start where you are.

Last week, my daughter started kindergarten. I’d imagine her confidence would be pretty shot if her teacher passed out tattered copies of War and Peace on the first day of class.

But no, kids first learn at an early age to sing their ABCs—the fundamentals of language—before learning to write their own names (or swear words on the wall, once they find Sharpies in the kitchen drawer).

Then they learn to repeat words in a book as you point, helping to form the connections between what they see and hear. Over time, kids progress to reading increasingly complex books.

The same confidence-building path applies when learning the language of data.

In our 7 years of training our team at Column Five, 4 years of teaching Visualization of Information at Columbia University, and now every day on Visage when new people sign up to create visual stories with all sorts of information, we’ve seen thousands of people improve their data communication skills. These fundamentals can be learned. The moment of first success, the “aha” moment when a data set comes to life, is quite a cool sight to behold.

For those of you whose understanding is already far beyond the basics, this post is for your colleagues who aren’t yet there. But just give them patience; they’re on their way.

What causes data illiteracy?

Data illiteracy largely exists because of fear:

  • “How do I use data in content?”
  • “What if I make a mistake?”
  • “What questions should I be asking?”

This fear is usually rooted in a lack of mastery of the language of data. We perceive the difficulty of going from zero to expert and resist the unknown.

Overcoming this fear starts with a solid definition of data literacy as a foundation.

What is data literacy?

Ask 100 people what data literacy means, and you’ll get 95 different responses. After all, there’s never 100% survey response rate. (OK, that was a bad data literacy joke.) But the point remains: People are confused.

To help clear up that confusion, the Data Journalism Handbook has a solid definition of data literacy:

“Just as literacy refers to ‘the ability to read for knowledge, write coherently and think critically about printed material,’ data literacy is the ability to consume for knowledge, produce coherently and think critically about data.”

In other words: Data Visualization 101.

Does all this talk about data mean you have to become a data scientist?

Short answer: No. Modern marketers don’t need to be data scientists, but they do need to be fluent in a shared language to be able to collaborate.

Marketers need to get to a place where they know how to ask more intelligent questions and understand why those questions need to be asked.

I see marketers learning to speak the language of their data teams—whether the team includes a formal data scientist or the engineer on a small team—so they know how to make a more intelligent data pull request. Brand marketers are motivated to unlock this gold mine of original content.

Here’s an example.

Product managers need to know how to speak the language of a developer or engineer, but they usually don’t need to be able to write JavaScript.

Product managers do need to have an understanding of the way the developer or engineer thinks and works, so they can capture the right information for the end user.

In the same way, the goal is to develop a shared language of collaboration with the data scientist, the data analyst, or anyone else on your team who can help you discover interesting information that only your brand can unlock (while staying cool on the privacy and security front).

This is not to say that you have to master the most advanced methods of working with data.

But you can take the lead and become more valuable to your organization by learning to speak competently to other people on your engineering or data team.

So how do you start learning about visual data literacy to become a better storyteller?

We’re focusing on communicating with data rather than diving into the world of data analysis, so these fundamentals are geared toward the basic needs of a marketer presenting data to an audience of any size.


Fundamental #1: Consume for knowledge

Understand the various ways to read your data.

  1. Nominal Comparison: Providing a simple comparison of the quantitative values of subcategories (e.g., number of visitors to various websites)
  2. Time Series: Tracking changes in values of a consistent metric over time (e.g., monthly sales)
  3. Ranking: Showing how two or more values compare to each other in relative magnitude (e.g., historic weather patterns, ranked from the hottest months to the coldest)
  4. Part-to-Whole: Showing a subset of data compared to the larger whole (e.g., percentage of customers who purchase various products)
  5. Deviation: Examining how data points relate to each other, particularly how far any given data point differs from the mean (e.g., amusement park tickets sold on a rainy day vs. on a regular day)
  6. Frequency Distribution: Showing data distribution, often around a central value (e.g., heights of players on a basketball team)
  7. Correlation: Comparing data with two or more variables that may demonstrate a positive or negative correlation to each other (e.g., salaries broken down by level of education)


Fundamental #2: Produce coherently

I like the perspective shared by tennis great Arthur Ashe, “Start where you are. Use what you have. Do what you can.”

Many times, people unrealistically expect to be able to upload a spreadsheet and visualize their data. It’s not quite that easy.

As you start applying your understanding of data relationships, you start getting a feel for the various types of data sets and why different measurements can’t be combined into a single chart type or visualization in an intelligent or useful way.

You end up realizing that a lot of data stories are often most clearly communicated through one of the core chart types.

People get tired of seeing bar charts, but they’re useful, easily perceived, and easily understood.

When you think about your viewers, you want to help them comprehend and retain the information you’re presenting.

Essentially, don’t knock yourself if your early efforts in data storytelling feel somewhat basic, as the goal is to master these fundamentals and build on a strong, confident foundation.


Fundamental #3: Think critically

The third component of data literacy is the ability to think critically.

Thinking critically means asking yourself:

  • Is the source of this data reliable?
  • How was this information collected?
  • Is there an inherent bias from a person/people who collected the data? (Hint: Yes, but what is it?)
  • What lengths have the authors of the data gone to in order to reduce (or eliminate) the bias in collecting the data?

Finally, double-check your data, and consider how powerful it is when presenting your supporting evidence from a certain perspective.

The whole aspect of checking the accuracy of your data is another way of thinking critically and not just assuming that the person who handed it off to you was infallible.

But what if you don’t use (or think you don’t need) visual data?

At this point, people fall into 1 of 2 camps:

  1. There are people who say, “Yes, absolutely—visual data is where the world’s headed; our entire team needs to get better at this to stay competitive.”
  1. Then the second camp thinks that, since they don’t create charts, they don’t need to be literate about visual data.

The second camp is having fun with inertia—it’s easier to keep doing what you’re doing rather than change course and learn a new skill.

Of course, the counter argument here is that part of your job as a marketer is to provide clarity and help your audience make sense of complex information. If you do this, you’re teaching your readers and helping them grow, while providing value.

If you’re already providing clarity in content by sharing your culture, describing your product, and giving step-by-step tutorials on how to do something, why not open up the world of data to support your perspective and win over your audience?

How to use visual data to become a better storyteller

When diving into a new project that might seem unfamiliar or overwhelming, set small, incremental goals.

To help you feel less overwhelmed, here are several ways to ease into that project and start becoming a better visual data storyteller.

  1. Hold an on-site workshop to educate your marketing team on the foundational principles of data visualization. This is a fun and efficient way to gain greater confidence as a data storyteller.
  1. Find, use, and explore the number of tools that are available for experimenting with data and visualizing data. Become familiar with the basic chart types by checking out our Data Visualization 101 series.
  1. Set up an experiment. For instance, write one data-driven blog post a month. Monitor the post’s analytics to see how well it does and learn what your audience thinks of it.

Data Visualization 101: How to Design Charts and Graphs [Free e-book]

You don’t need a Ph.D. or mathematics degree to crack the visualization code.

Download our free e-book, Data Visualization 101: How to Design Charts and Graphs, to start creating compelling visual data stories.

You’ll discover how to:

  • Find the story in your data
  • Know your data
  • Distinguish different chart types

There’s also a bonus chapter: “10 Data Design Dos and Don’ts.”

Like any new marketing effort, establishing a reputation for telling interesting and original visual data stories takes time. Educating yourself enables you to find surprising, valuable answers to good questions with your data team for your visual content marketing.

Remember: Start where you are, and watch your confidence grow as you tackle a few experiments and get some early wins. You’ll be analyzing the likes of War and Peace in no time.