When I’m travelling, I love a good book. There is no better way to kill hours on a plane (or in a train, or in an airport, for that matter) than poring over the pages of something really engaging. But different narrative styles work better for different kinds of stories. The information you want to highlight — and the audience you want to highlight it to — will dictate how you tell a tale. The same is true for data stories.
7 Types of Data Stories
Tableau’s Ben Jones presented his thoughts on the seven types of data stories a few years back. At Datassist, the majority of the stories we work on fall into one of these categories. Determining what type of data story you’re telling is a useful way to frame what you’re using your data for. (It’s essential to clarify what data perspective your data story is using.)
So how do you know which to use?
In this post, I’ll explain the differences between various types of data stories. To illustrate, I’ll use examples of different data stories that can all be told using the same dataset. In this case, I’ve selected World Bank Findex data on financial inclusion in the United States. This data captures a nationally representative sample of individuals and asks them about their banking and financial activities. (This same survey was also administered in many other countries.)
Let’s get started!
Change Over Time
One way to tell a data story is to examine how a trend changes in interesting or meaningful ways over time. This generally works best if:
- You have data over a significant period of time
- There is a change — either gradual and meaningful or sharp and unusual
This type of story is typically easy to visualize and easy to understand conceptually. Using our World Bank Findex data, we could tell a change over time data story by looking at the percentage of people who have any kind of financial account. In the US, the number is high and still climbing over time.
Another way to tell your data story is to analyze the specific details of what’s happening in a trend you see. Drilling down works best when:
- You have layered data
- There is a complex trend you can examine
This type of data story is often more unique than simpler narratives, but beware: it requires more analysis as well. Drilling down into the Findex data, we can look at how the percentage of people with accounts differs between social groups. (For example, young people with accounts)
Zooming out is kind of the opposite of drilling down for your data story. This method choosing a data point and comparing it widely with others. Typically, zooming out:
- Incorporates other, broader datasets
- Shifts the perspective of the reader
Telling your data story by zooming out can add meaning to information that might otherwise seem meaningless. It can offer a broad sense of appeal — or promote a strong sense of division, depending on how it’s used. We can apply this data story technique to our Findex data to learn whether the financial situation in the US is better or worse than average. By using data from other countries to zoom out, we can show how our story is situated in a wider context.
Comparing one result or dataset to another can be an effective way to tell a data story that surprises your audience. Contrast works best when:
- You have one dataset with lots of variables, or
- More than one similar dataset
This is one of the loosest forms of data story. It leaves readers plenty of room to apply their own interpretations to the data. It can be very effective when offering a counterintuitive result. Using our Findex data, we can tell a contrast story about how the rate of access to formal accounts relates to having enough money to cover basic daily necessities.
If you want to convey how different dimensions are working together within a trend in your data, you may want to use an intersectional story. For this type of data story, you’ll:
- Need data at a fairly granular level
- Demonstrate how different groups and subgroups in your data differ
Intesectional stories are the most inclusive type of data story and can add nuance to already well-understood situations. However, they do require some fairly complex analysis to develop. We can create an intersection story using our Findex data to convey the complex way variables interact to tell a more interesting story. We’ve created a narrative about how gender, age, and education work together to influence the likelihood a person has a financial account. The example below shows how gender affects financial inclusion in the US.
Many great stories are developed around examining the cause of a specific event or phenomenon. Factor data stories look at what might be influencing your data or trend. These stories:
- Require the most data AND the most analysis
- Are the most difficult to do accurately
Factor stories are often the most sought-after by readers, but are also the most prone to common statistical analysis traps. We could tell a factor story with the Findex data by examining the fact that income bracket appears to be a powerful factor in determining how much access people have to formal bank accounts.
Sometimes outliers in your data represent errors and need to be rejected. But some interesting outliers can become the basis for a new data story. Outliers:
- Require minimal, basic data
- Don’t require an explanation to be interesting
This type of data story is generally light and easy to understand. Outliers often only appeal to a niche audience, but that can work in your favour if you have specific story goals. They’re very easy to visualize. We can use the Findex data to explore the relationship between financial inclusion and trust in banks across different countries. This graph highlights several interesting outliers — each a potential data story.
Want Help Telling Your Data Story?
At Datassist, we’ve partnered with journalists, nonprofits, and even government agencies to help them tell their data story. Whether you need help with data collection, analysis, or storytelling, our team is at your service. Get in touch today.