We live in a world flooded with data. Data has become the raw material from which any other type of business can be built and for years, data has been called the “oil of the 21st century.”
By 2025, it is estimated by the IDC that the world’s data will grow to 175 zettabytes by 2025. What do you think 175 zettabytes looks like?
- If you stored 175 zettabytes on DVDs, your stack of DVDs would circle the globe 222 times
- If you tried to download 175 zettabytes on the average internet connection, it would take 1.8 billion years
- If you enlisted everyone in the world to help download 175 zettabytes of data, it would still take 81 days
Okay, so there is a massive amount of data out there. But how do you take the data you’ve collected and turn it into something that is digestible for viewers?
What is data visualization?
Data visualization is a form of visual art that transforms tables and spreadsheets into visuals to help users observe patterns or trends in the data being analyzed.
Instead of writing out those trends as part of overly dense paragraphs containing stats, visualization makes data consumption and analysis quick and easy.
It also makes for some of the best visual content. Visual content should be more than a header with cute cartoons, and data visualization is a blend of form and function.
Data visualization can be complex, but it can also be simple:
- Complex – 3D medical imaging, urban transportation simulations, disaster monitoring, etc.
- Simple – Graph showing emails opened by location, profit growth over time, etc.
A good data visualization summarizes information and organizes it in a way that lets the reader focus on the relevant message and key points.
Why learn to create data visualizations?
Data is relevant to every industry and so is relevant to every marketer. It should be the basis of visual content and is a skill that every Creative should consider adding to their toolbelt.
This is even more true in the world that comes after the COVID-19 shutdown. Data will continue to fuel the internet and countless businesses that use it to make money.
Marketers with data visualization skills will be that much more valuable in the workforce in 2021 and going forward.
Six Courses on Data Visualization
Today, the only thing holding you back from being able to learn how to create incredible data visualizations is your ability to manage your time.
A quick search revealed a number of different courses on data visualization from some of the biggest higher education institutions. Here are some of the free courses I am interested in taking myself:
- Harvard – Data Science: Visualization
- Duke – Data Visualization and Communication with Tableau
- University of Illinois – Data Visualization
- Trinity College – Data Visualization for All
- University of Michigan – Understanding and Visualizing Data with Python
- MIT Open Courseware – How to Process, Analyze and Visualize Data
But clearly, these courses are a bit involved, even if they require no monetary commitment. Here are some simple steps for getting your brain going about data visualization.
What are the first steps of creating a data visualization?
First, determine the data or statistic set you want to turn into a visualization. Then ask, “How will the visualization help the reader?”
That’s an important question to answer, you want to practice creating data visualizations that strike the right balance of data analysis and compelling storytelling. Here are some other introductory questions to ask:
- What variables are you trying to plot?
- What do the x- and y-axis refer to?
- Does the size of the data points have meaning?
- Will a particular color tell the story better?
- Are you identifying trends over time or displaying the correlation between variables?
What are some common types of data visualization?
Here are some common forms of data visualization:
Full disclosure, I didn’t make this up.
I watched one video a few years ago about data visualization. The first exercise was to figure out a data visualization of the morning commute.
Obviously, with so many people working from home, that has changed quite a bit. But you can sit and consider all the different ways you can quantify and visualize your morning commute.
For my commute in the before, I thought it would be interesting to visualize it by elevation. I live on the opposite bank of a river valley from HIPB2B’s physical HQ, so seeing it visually would be interesting.
The instructor recommended this exercise to sketch out different forms of visualizing simple sets of data. What does your morning commute look like?
What do you think? Is data visualization something you’re going to level up your skills on this upcoming year? What will you use it for?