What is 'data storytelling'?
"With data storytelling" is a phrase that is often used in discussions related to data. It is no longer limited to data scientists, analysts, or technical teams. It is becoming a skill that a diverse group of employees is expected to engage with in some way. In 2022, Microsoft described data storytelling as “the concept of building a compelling narrative based on complex data and analytics that help tell your story and influence and inform a particular audience.”
That definition is a bit technical, but for most of us, data storytelling shows up in slightly different ways in our day-to-day work. It is less about building complex narratives and more about being able to engage with data in a way that supports decisions, helps us ask better questions and makes sense of the data we're working with. And to be honest, it's not something that comes naturally to most of us.
A skill that can feel difficult at first
It might be a tricky skill to develop but that doesn't mean it is out of reach. We can all engage with data storytelling at a level that makes sense for our role, our team, and our organisation. Like most skills, it improves with practice. We are not fixed in our abilities, and with effort, we can learn and get better over time (Dweck, 2006).
The challenge, though is knowing where to start and what to focus on. There are a few key pieces that can make a difference, and it is worth spending some time with them if you want to build your data storytelling skills.
Beginning with data literacy
The first element is data literacy. Before you can do anything meaningful with data, you need to understand what you’re actually looking at. The data we work with in our organisations has a specific meaning, and that meaning is determined by context. The numbers in one dataset may not mean the same thing in another. They are tied to the context they come from and the way they have been measured. Without that basic understanding, it is very difficult to use data.
As Charles Seife points out in Proofiness: How You’re Being Fooled by the Numbers (2010)
"... numbers are interesting only when they give us information about the world. A number only takes on any significance in everyday life when it tells us how many pounds we've gained since last month, or how many dollars it will cost to buy a sandwich, or how many weeks are left before our taxes are due..."
So when we look at something like percentage profit, staff turnover, or year-on-year growth, these figures require a slightly different kind of understanding. They are not interchangeable, even though they may all be presented as percentages. If we want to use them well, we need to take the time to understand what they represent and how they behave.
Building data literacy is not easy. There is rarely a single place where everything you need is explained in a way that suits your organisation. Most of the time, you come across training, resources, or explanations that help build understanding over time. It can take a bit of effort, but it is a necessary step if you want to move beyond simply looking at data to actually working with it.
How Visualisation Helps Us Read Data
Once we have a reasonable level of understanding of the data itself, the next piece of the puzzle tends to be how that data is presented. This is where data visualisation plays a crucial role. Data visualization is "data that is presented in graphs, tables, or other images to make the trends easier to identify, and to reduce the cognitive load of engaging extensive sets or lists of raw data" (Fisk, 2022), and what we know about good visualisations is that there is an art and science to developing them (Knaflic, 2015).
Different types of visuals work better for different types of data, and some formats are easier for people to interpret than others. For example, line graphs and bar charts look familiar because we see them regularly in the media and in everyday reporting. We’re used to reading them, which makes it easier to spot trends or changes. Other types of charts, such as box plots or waterfall charts, are less common and often less intuitive for people to interpret. That doesn’t make them less useful, but it does mean they require a bit more effort and understanding.
The point where data storytelling starts making sense
When we have solid knowledge of both data literacy and data visualisations, we are in a much better position to learn data storytelling. This is where the rubber hits the road in terms of using the data in your work, in your team, and to inform your decisions. While it is recognised that data storytelling is key to using data well, some of the research about this practice suggests that only one in 10 companies engage with data storytelling on a regular basis (Tischler et al., 2017).
At the same time, there is also evidence that organisations are not investing enough in developing these skills among their employees (Amini et al., 2018).
The two questions that really matter
When we engage in data storytelling, we seek to answer two key questions through the process:
- What trends and insights can we see in the data?
- What do we do about those trends and insights?
Both of these questions can be more challenging than they first appear. It takes practice to distinguish between data that is informative and data that is insightful. We often have access to large amounts of information, but not all of it points to something meaningful or actionable.
Then there is the second question, what to do with those insights. Even when we have a firm grasp of what the data is telling us, translating that into action is another step. It involves thinking about what is within our control, what influence we have, and how we might involve others in making decisions based on that information.
This is where collaboration become important. Bringing other people into the conversation can help generate ideas, challenge assumptions, and create a clear path forward. The more brains, the better!
FAQ
1. What is data storytelling in simple terms?
Data storytelling is the process of using data, visuals, and narrative to explain insights in a way that helps people understand information and make better decisions. It combines data literacy and data visualisation to turn raw data into meaningful action.
2. Why is data storytelling important in the workplace?
Data storytelling is important because it helps teams move beyond raw numbers and focus on insights that support decision-making. It improves communication, helps non-technical teams understand data, and supports better collaboration across an organisation.
3. What is the difference between data literacy and data storytelling?
Data literacy is the ability to understand and interpret data correctly, while data storytelling is the ability to communicate insights from that data in a clear and meaningful way. Data literacy comes first, and storytelling builds on it.
4. How does data visualisation support data storytelling?
Data visualisation supports data storytelling by turning complex datasets into graphs, charts, and visuals that are easier to understand. It reduces cognitive load and helps people quickly identify patterns, trends, and insights.
Conclusion
Data storytelling may not come easily or naturally to you, and that’s okay, it's something that everyone can get better at. It's not until we engage in data storytelling, however, that we actually put our data to work. Without data storytelling, data is just a whole lot of effort, a whole lot of collection and storage, but not a whole lot of impact.
References
Amini, F, Brehmer, M, Bolduan, G, Elmer, C & Wiederkehr, B (2018). Evaluating data-driven stories and storytelling tools. In N. Riche, C. Hurter, N. Diakopoulos & S. Carpendale (Eds.), Data-driven storytelling (pp. 249-286). A K Peters/CRC Press.
Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.
Fisk, S. (2022). I'm not a numbers person: How to make good decisions in a data-rich world. Major Street Publishing.
Knaflic, C.N. (2015). Storytelling with data: A data visualisation guide for business professionals. John Wiley & Sons.
Microsoft (2022). What is data storytelling? https://powerbi.microsoft.com/en-us/data-storytelling#.-text-Data%20storytelling%20is20the20concept.and%20inform%200%20particular%20audience
Seife, C (2010). Proofiness: How you're being fooled by the numbers. Penguin
Tischier, R. Mack. M & Vitsenko, J (2017). BARC research study: Interactive analytical storytelling. www.sitsi.com/download /25919/185961/?ct=1
