Click on the numbered tabs to navigate this lesson.
Data, visualisation and narrative
In the visualisation above, created for the Swamp City project, we see the value and the benefits of wetlands in Kampala, Uganda that has been modified, mainly due to industrial development and small-scale farming.
Storytelling is a natural element of human communication and interaction. Data stories share much in common with other kinds of stories that we encounter every day.
The difference is that data storytelling centres around data and its presentation. Data stories have three important elements: data, visualisation and a narrative.
In this lesson, you will learn:
- Identify the components of an effective story
- Understand how data stories can have an impact
- Identify patterns in data that make compelling stories
- Identify visualisation techniques to engage audiences
- Build a narrative that relates to an audience
Why tell stories with data?
Data by itself is often not very compelling or interesting and can be hard to remember. But stories command our attention. A good data story explains how data is relevant to an audience, and what they should do about it.
Data can make storytelling more effective because:
Data presented in story form is more compelling than numbers alone. Data can provide support for other evidence (such as an expert comment or anecdotal evidence).
Data can raise awareness
Data stories can highlight important and compelling issues that might otherwise get lost in a sea of information.
Data stories can foster engagement
Data-driven storytelling can make data more interesting, relevant and personal. One can also ask readers to share their own experiences as data, such as in this example from OpenUp, asking people to share information about what they pay domestic workers.
Data is a call to action
Data stories can compel an audience to take action, or adopt a different point of view.
Data for storytelling
There are three elements of a data-driven story: the data, the visualisation, and the narrative. This compelling image created by PesaCheck manages to combine all three in one graphic, explaining if it’s possible for cyclones to hit Kenya.
We’ll start with the data. Earlier lessons in this course taught you how to find, evaluate, download and analyse open data. Preparing data for a story employs all of these skills.
Remember that data analysis starts by developing a question that you want your data to answer. This question will provide the centrepiece of your data story.
You can think of your research question as being the scene of your data story, and the data itself as the characters that bring the story to life.
Finding the story
As you conduct your data analysis, look for characteristics that might help develop your narrative, and provide interest. In the interactive visualisation above, you can see some of the characteristics in a dataset that might help develop your story. This Valentine’s Day special by OpenUp in South Africa documents the process of finding a story in data perfectly.
Patterns and trends
How does your data change over time? Are conditions getting better, worse, or shifting from one category to another? Are these changes connected to policy decisions or other events?
Similarities, contrast and outliers
In what ways are groups within your data similar or different? Are some groups performing above or below average? Are there outliers that are substantially larger or smaller?
Interesting or unusual exceptions
What are the individual values that fall outside the norm? What might explain these exceptions?