What is Data Storytelling?
Data storytelling is a technique that transforms available data into a storyin a simple, concise way. It combines data visualization formats (such as graphs, charts, animated maps, and so on). It is a useful way to present insight — which means it can be used with both internal and external audiences.
Why Data Storytelling?
Data storytelling is a structured approach for communicating data insights, and it involves a combination of three key elements: data, visuals, and narrative.
It’s important to understand how these different elements combine and work together in data storytelling. When narrative is coupled with data, it helps to explain to your audience what’s happening in the data and why a particular insight is important. When visuals are applied to data, they can enlighten the audience to insights that they wouldn’t see without charts or graphs. Many interesting patterns and outliers in the data would remain hidden in the rows and columns of data tables without the help of data visualizations.
Finally, when narrative and visuals are merged together, they can engage or even entertain an audience. It’s no surprise we collectively spend billions of dollars each year at the movies to immerse ourselves in different lives, worlds, and adventures. When you combine the right visuals and narrative with the right data, you have a data story that can influence and drive change.
There are 4 steps to be followed before data storytelling
Step 1. Be Clear on the Question.
When creating a visualization, the first step is to be clear on the question to be answered — or alternatively answering the question: “How will the visualization help the reader?”
Step 2. Know Your Data and Start with Basic Visualizations
The next step after identifying the visualization’s objective is building a basic diagram –this can be a bar chart, line chart, flow chart, scatterplot, surface plot, map, networks, and more — depending on what data are available. In the course of identifying the key message or messages the chart should convey, we must be clear about several things:
- What variables are we trying to plot?
- What do the x-axis and y-axis refer to?
- Does the size of data points mean anything?
- Does the colour in the chart mean anything?
- Are we trying to identify trends over time or correlation between variables?
Step 3. Choose the right Chart Type
Step 4. Use Colour, size, scale, shape and labels to direct attention to the key messages.
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