People often equate data science with statistics, but it’s so much more than that. When data science first emerged as a craft, it was a combination of three different skill sets: science, mathematics, and art. But over time, we’ve drifted. We’ve come to prioritize the scientific side of our skillset and have lost sight of the creative part.
One of the most neglected, yet arguably most important, skills from the artistic side of data science is communication. Communication is key to everything we do as data scientists. Without it, our businesses won’t be able to understand our work, let alone act on it.
Being a good data storyteller is key to being a good data scientist. Storytelling captures your stakeholders’ attention, builds trust with them, and invites them to fully engage with your work. Many people are intimidated by numbers. By framing a narrative for them, you create a shared foundation they can work from. That’s the compelling promise of data storytelling.
Data science is a balancing act—math and science have their role to play, but so do art and communication. Storytelling can be the binding force that unites them all. In this article, I’ll explore how to tell an effective data story and illustrate with examples from our practice at Shopify. Let’s dive in.
What Is Data Storytelling?
When you Google data storytelling, you get definitions like: “Data storytelling is the ability to effectively communicate insights from a dataset using narratives and visualizations.” And while this isn’t untrue, it feels anemic. There’s a common misconception that data storytelling is all about charts, when really, that’s just the tip of the iceberg.
Even if you design the most perfect visualization in the world—or run a report, or create a dashboard—your stakeholders likely won’t know what to do with the information. All of the burden of uncovering the story and understanding the data falls back onto them.
At its core, data storytelling is about taking the step beyond the simple relaying of data points. It’s about trying to make sense of the world and leveraging storytelling to present insights to stakeholders in a way they can understand and act on. As data scientists, we can inform and influence through data storytelling by creating personal touch points between our audience and our analysis. As with any good story, you need the following key elements:
- The main character: Every story needs a hero. The central figure or “main character” in a data story is the business problem. You need to make sure to clearly identify the problem, summarize what you explored when considering the problem, and provide any reframing of the problem necessary to get deeper insight.
- The setting: Set the stage for your story with context. What background information is key to understanding the problem? You're not just telling the story; you're providing direction for the interpretation, ideally in as unbiased a way as possible. Remember that creating a data story doesn’t mean shoe-horning data into a preset narrative—as data scientists, it’s our job to analyze the data and uncover the unique narrative it presents.
- The narrator: To guide your audience effectively, you need to speak to them in a way they understand and resonate with. Ideally, you should communicate your data story in the language of the receiver. For example, if you’re communicating to a non-technical audience, try to avoid using jargon they won’t be familiar with. If you have to use technical terms or acronyms, be sure to define them so you’re all on the same page.
- The plot: Don’t leave your audience hanging—what happens next? The most compelling stories guide the reader to a response and data can direct the action by providing suggestions for next steps. By doing this, you position yourself as an authentic partner, helping your stakeholders figure out different approaches to solving the problem.
Here’s how this might look in practice on a sample data story:
| Main Character | Setting | Narrator | Plot |
| Identify the business question you're trying to solve. | What background information is key to understanding the problem. | Ensure you're communicating in a way that your audience will understand. | Use data to direct the action by providing next steps. |
| Ex. Why aren't merchants using their data to guide their business decisions? | Ex. How are they using existing analytic products and what might be preventing use? | Ex. Our audience are busy execs who prefer short bulleted lists in a Slack message. | Ex. Data shows merchants spend too much time going back and forth between their Analytics and Admin page. We recommend surfacing analytics right within their workflow. |
With all that in mind, how do you go about telling effective data stories? Let me show you.
1. Invest In The Practice Of Storytelling
In order to tell effective data stories, you need to invest in the right support structures. First of all, that means laying the groundwork with a strong data foundation. The right foundation ensures you have easy access to data that is clean and conformed, so you can move quickly and confidently. At Shopify, our data foundations are key to everything we do—it not only supports effective data storytelling, but also enables us to move purposefully during unprecedented moments.
For instance, we’ve seen the impact data storytelling can have while navigating the pandemic. In the early days of COVID-19, we depended on data storytelling to give us a clear lens into what was happening, how our merchants were coping, and how we could make decisions based on what we were seeing. This is a story that has continued to develop and one that we still monitor to this day.
Since then, our data storytelling approach has continued to evolve internally. The success of our data storytelling during the pandemic was the catalyst for us to start institutionalizing data storytelling through a dedicated working group at Shopify. This is a group for our data scientists, led by data scientists—so they fully own this part of our craft maturity.
Formalizing this support network has been key to advancing our data storytelling craft. Data scientists can drop in or schedule a review of a project in process. This group also provides feedback and informed guidance on how to improve the story that the analysis is trying to tell so communications back to stakeholders is most impactful. The goal is to push our data scientists to take their practice to the next level—by providing context, explaining what angles they already explored, offering ways to reframe the problem, and sharing potential next steps.
Taking these steps to invest in the practice of data storytelling ensures that when our audience receives our data communications, they’re equipped with accurate data and useful guidance to help them choose the best course of action. By investing in the practice of data storytelling, you too can ensure you’re producing the highest quality work for your stakeholders—establishing you as a trusted partner.
2. Identify Storytelling Tools And Borrow Techniques From The Best
Having the right support systems in place is key to making sure you’re telling the right stories—but how you tell those stories is just as important. One of our primary duties as data scientists is decision support. This is where the art and communication side of the practice comes in. It's not just a one-and-done, "I built a dashboard, someone else can attend to that story now." You’re committed to transmitting the story to your audience. The question then becomes, how can you communicate it as effectively as possible, both to technical and non-technical partners?
At Shopify, we’ve been inspired by and have adopted design studio Duarte’s Slidedocs approach. Slidedocs is a way of using presentation software like PowerPoint to create visual reports that are meant to be read, not presented. Unlike a chart or a dashboard, what the Slidedoc gives you is a well-framed narrative. Akin to a “policy brief” (like in government), you can pack a dense amount of information and visuals into an easily digestible format that your stakeholders can read at their leisure. Storytelling points baked into our Slidedocs include:
- The data question we’re trying to answer
- A description of our findings
- A graph or visualization of the data
- Recommendations based on our findings
- A link to the in-depth report
- How to contact the storyteller
Preparing a Slidedoc is a creative exercise—there’s no one correct way to present the data, it’s about understanding your audience and shaping a story that speaks to them. What it allows us to do is guide our stakeholders as they explore the data and come to understand what it’s communicating. This helps them form personal touchpoints with the data, allowing them to make a better decision at the end.
While the Slidedocs format is a useful method for presenting dense information in a digestible way, it’s not the only option. For more inspiration, you can learn a lot from teams who excel at effective communication, such as marketing, PR, and UX. Spend time with these teams to identify their methods of communication and how they scaffold stories to be consumed. The important thing is to find tools that allow you to present information in a way that’s action-oriented and tailored for the audience you’re speaking to.
3. Turn Storytelling Into An Experience
The most effective way to help your audience feel invested in your data story is to let them be a part of it. Introducing interactivity allows your audience to explore different facets of the story on demand, in a sense, co-creating the story with you. If you supply data visualizations, consider ways that you can allow your audience to filter them, drill into certain details, or otherwise customize them to tell bigger, smaller, or different stories. Showing, not telling, is a powerful storytelling technique.
A unique way we’ve done this at Shopify is through a product we created for our merchants that lets them explore their own data. Last fall, we launched the BFCM 2021 Notebook—a data storytelling experience for our merchants with a comprehensive look at their store performance over Black Friday and Cyber Monday (BFCM).
While we have existing features for our merchants that show, through reports and contextual analytics, how their business is performing, we wanted to take it to the next level by giving them more agency and a personal connection to their own data. That said, we understand it can be overwhelming for our merchants (or anyone!) to have access to a massive set of data, but not know how to explore it. People might not know where to start or feel scared that they’ll do it wrong.
What the BFCM Notebook provided was a scaffold to support merchants’ data exploration. It’s an interactive visual companion that enables merchants to dive into their performance data (e.g. total sales, top-performing products, buyer locations) during their busiest sale season. Starting with total sales, merchants could drill into their data to understand their results based on products, days of the week, or location. If they wanted to go even deeper, they could click over the visualizations to see the queries that powered them—enabling them to start thinking about writing queries of their own.
Turning data storytelling into an experience has given our merchants the confidence to explore their own data, which empowers them to take ownership of it. When you’re creating a data story, consider: Are there opportunities to let the end user engage with the story in interactive ways?
Happily Ever After
Despite its name, data science isn’t just a science; it’s an art too. Data storytelling unites math, science, art, and communication to help you weave compelling narratives that help your stakeholders comprehend, reflect on, and make the best decisions about your data. By investing in your storytelling practice, using creative storytelling techniques, and including interactivity, you can build trust with your stakeholders and increase their fluency with data. The creative side of data science isn’t an afterthought—it’s absolutely vital to a successful practice.
Wendy Foster is the Director of Engineering & Data Science for Core Optimize at Shopify. Wendy and her team are focused on exploring how to better support user workflows through product understanding, and building experiences that help merchants understand and grow their business.
Are you passionate about data discovery and eager to learn more, we’re always hiring! Visit our Data Science and Engineering career page to find out about our open positions. Join our remote team and work (almost) anywhere. Learn about how we’re hiring to design the future together—a future that is digital by design.