As technology advances, the amount of data collected and processed also increases. Data can bring big businesses significant profits, but the problem is that it can be hard to discern the data you need with the abundance of data. This is where data visualization comes in. Taking digital data and turning it into visuals like graphs can make a massive difference in understanding data and getting the valuable insights you need.
What Story-Driven Data Visualization Does
Data visualization simply relates the measured metrics in a visual way, most commonly with charts and graphs so that instead of pages of spreadsheets, viewers see the important insights the data give.
Story-driven data visualization does the same, but instead of only providing context to the data, provides the big picture of what the data says, guiding the viewer through key data points to a specific conclusion.
Examples:
How to Create Story-Driven Data Visuals
It takes 7 steps to create these visuals:
Evaluate the Data Set
Give Purpose to the Data
Identify the Key Plot Points
Create the Narrative
Construct the Outline
Choose the Best Visual Format
Put Everything Together
Evaluate the Data Set
The very first thing to do is understand what the data you’re reporting on says, in its very dry way. Ask Yourself:
● Are there surprises in my data? Unexpected results?
● Is the data showing any trends or recurring themes?
● Is the data set pointing to a larger trend?
● Are there key characters in the data? People, businesses, or factors that greatly impact the data? Someone/something in the data that stands out?
The answers to these questions will give you the plot points of your story that will either give you a chronological plot (story arc), or a story that narrates and expands each plot point.
Give Purpose to the Data
When you know what the data is pointing to it’s time to figure out the big picture of the data and how you’re going to relate it to your target audience. Ask yourself:
● Who is my intended audience?
● What information do they care about?
● How much detail do they need from the data? How many variables do you need to show?
● What am I trying to accomplish with my displayed data?
● What is the big picture the data tells me? What do I want my audience to know and/or do with the data I give them?
● How will my data help the audience come to a decision?
These questions will help you know which data points are important when you construct the narrative framework.
Create the Narrative
With your subject and plot points that clearly show the direction of the story the data relates, tie those characters and plot points together.
This can be done in a simple story arc, where you show, chronologically, the
● Exposition: establishing the characters involved and their setting, with an “inciting incident,” or deviation in the data than what was expected.
● Rising action: the collection of data points that support the trend or recurring theme you noticed.
● Climax: The main insight the data revealed.
● Falling Action/Resolution: The recommendation the visual gives, based on the data. For example, companies that have curbside pick-up have 10% greater revenues.
● Resolution: Based on the data revealed in the visual, the audience should be able to naturally come to the intended conclusion, such as “This company is losing sales because they don’t have curbside pick-up.”
Knowing your narrative, determine the key data points that most clearly further the story’s narrative. In other words, the points that align with your message and directly supply information supporting your desired conclusion.
Choose the Best Visual Format
Based on the message you’re trying to relate, what is the best way to visualize your information? Do you need a traditional chart or graph to make a comparison, identify compositions of a whole, identify a relationship, or determine the distribution of goods or other variables? Or can your story be told less traditionally, and be told clearly with simply a picture, enlarged text, and some bullet points? The format that clearly expresses your message as concisely as possible is what you want.
Conclusion
At last, you can put it all together! Don’t worry if this seems extremely complicated. It will get easier with practice as you get faster at selecting data and understanding how each visual display relates the same information differently.
Written By
Comments