Episode 13: 7 Different Types of Data Stories

7 Different Types of Data Stories

Have you heard about these 7 different types of data stories? In this week’s episode, I review this data storytelling framework with you so you can use this anytime you feel stuck on what story to tell with your data. I’ve also linked a helpful article with data visualization examples for each of these in the show notes.

You can also listen on Apple Podcasts, Google Podcasts, and Spotify.

What You’ll Learn in this episode

  • A framework for crafting a data story
  • What each data story type is
  • How to make each data story actionable for yoru audience

Additional Resources

Get in Touch with Hana

Let me know what you think of the episode, you can message at hana@trending-analytics.com or on Instagram @hanalytx.

If you are looking for podcast updates and want additional tips on how to visualize and present data sent straight to your inbox, then make sure to subscribe to my weekly data letters here.

When you hit that subscribe button, I’ll be sliding into your inbox every Wednesday with an email.

Love the show? Why not leave a review?

If you loved this episode of the Art of Communicating Data Podcast, why not leave a review on Apple Podcasts and Spotify?

It only takes 2 minutes and provides me with invaluable insight as to what the listeners think.

If you enjoyed this episode, check out this episode where I share tips on ways you can improve your data visualizations.

Episode Transcript

In this week's episode, I want to share with you seven different types of data stories that Ben Jones came up with. It's a framework he developed while at Tableau.  If you've never heard of them before, listen to this episode, I find these helpful to keep in mind. 

Especially, if you feel stress about not being able to come up with a story. I think sometimes we also tend to overcomplicate things and this framework is a really good reference. If you ever feel stuck. 

These seven different types of data stories are not meant to be exhaustive, but you can use them as a starting point. 

As you go through each of these types, I want you to remember to keep your target audience. 

And the main message that you want them to take away with in mind when deciding which type of data story to go with. All right. So let's get started with the first type, which is showing change over time. You can focus on. One variable. And show how it changes over time. You can point out any trends, outliers, or inflection points that you may have discovered in your data. 

 To make this data story actionable point out to your audience, what successes or failures from the past that they can learn from, and maybe what they can do in the future. So this helps make that data story actionable.

The second type of data story is starting big and drilling down. So that means starting broad and then going deeper. This is a good story to go with. If you have a complex trend you want to deep dive into, or a broader context, you want your audience to be aware about when making decisions about a specific factor that you drill down on. 

So an example of this, as you can maybe start out with a map, providing a big view, and then the audience can drill down on maybe a regional level or country level. 

You can also go in the other direction by starting your focus with one particular area of the data and then zooming out to compare it with the rest of the data. So this is the third type of data story, which is zooming out. If you're deciding between this strategy of starting at the micro level and zooming out versus the previous data story that we talked about. 

Which is starting broad and then drilling down. I recommend trying both narratives with yourself or with some colleagues to see which one is more effective in getting your main idea across. 

The fourth type is highlighting contrast in the data. Maybe you want to show how things are different between categories. 

So this data story works. If you have data with different variables or multiple similar data sets where you can highlight contrast. And you can make this actionable by advising what can be done to make these categories maybe more aligned if that's the goal. Like how to make one category of a product perform as well in terms of profits. 

Like a, another well-performing product category. That's just an example. 

Another interesting story can be found at the intersection point where one category overtakes another, depending on if this shift is good or bad. Your audience could be motivated by the story to either prevent or continue doing something. To maybe avoid this shift in the future or bring it out again, If this shift happened at a certain point in time. You can do a combination and use the first type of story. To slowly show what happens over time that led up to this intersection point. 

The sixth type of data story is dissecting the factors. This is when you have one main metric and you try to explain the various factors that could be influencing it the most. Like showing correlations or causations between the different factors. 

Using the story can help your target audience, see which factors we should focus on the most  or how we can use one or more of these factors to predict or control the main metric that we care about. This story can help your target audience prioritize different factors. If you're working with a lot of them. And we'll help them focus their efforts in the future, and also figure out which ones are not as important to focus on. 

  Finally, one of my favorite story types is profiling outliers. This is when you find a data point or few that are substantially different from the rest. If this outlier is relevant to the main message that you want your audience to take away with. You can use a story type to show what makes this data point different? Is this a good or a bad thing and what can be done to promote or mitigate this from showing up in the future? 

You can also use a combo. Of other data story types to explain or explore this outlier further. Well, I hope this helps you with crafting your next data story.

I will also link in the show notes, an article written by Martha King, who works at Tableau and was a colleague of Ben's. And then this article, she actually has really great examples for each of these data story types, with data visualizations that I think will be helpful for you. If you had trouble understanding any of these data stories. 

This is also the end of season one. But don't worry. Season two of this podcast will be out in just a few weeks. While the podcast is on a break. I won't be, I'll actually be conducting interviews with guests for the second season. And I am really excited for what's coming next. If you would like to suggest a topic I cover related to data communication in the upcoming seasons, then please feel free to email me at Hannah, H a N a. At trending. Hyphen analytics.com. I'll also still be regularly posting content on my social media platforms and my mailing list. You can subscribe to my mailing list by going to trending hyphen analytics.com. Forward slash subscribe. If you want to follow me on social media, you can find my handles listed in the show notes. 

It is Hannah Lytics spelled as H a. N a. L Y T X on Instagram and tic talk. And you can also find me on LinkedIn. I'll have that linked in my show notes.