With the help of data analytics platforms, it has become easy to access data in a few clicks. However, regardless of the platforms, businesses cannot fully exploit the insights hidden beneath the data due to the lack of one skill: data storytelling.
“Two skills of modern business: Storytelling and spreadsheets. Know the numbers. Craft the narrative.”– James Clear, author of Atomic Habits
In this article, we dive deeper into what data storytelling is, why it is important, and provide a few tips on boosting your data storytelling skills.
What is data storytelling?
Data storytelling is often associated with creating visually appealing graphics, such as graphs, charts, and more. However, it is beyond that. Data storytelling is a structured approach to communicating a compelling narrative based on data to develop new decisions and actions.
Why is data storytelling important?
We all know that data is essential. However, the numbers alone become insignificant. With data storytelling, results become actionable and impactful for the audience. Data storytelling uses data to drive change by guiding the audience to understand the insights beneath the numbers.
For example, maybe you are working with your team to analyse the performance of patterns. What if the data was presented to you like this:
Would you understand what each bar represents? Unfortunately, no, you wouldn’t, as there is no context supporting these numbers.
That is where the “storytelling” part comes in to help the numbers make sense. Instead, what if data was presented to you like this:
Now, with the context revealed, you can craft the story:
“At the peak of summer during June 1, 2021, to July 27, 2021, Revolve (US) mostly stocked animal print products and is shown to meet consumer demand as the top of the product contribution bar matches the sell-out contribution line.
Nonetheless, there appear to be missed opportunities in floral as the sell-out contribution line, at 7.94%, lies above the product contribution bar, at 6.70%, representing demand. Looking at the most replenished floral patterned items, we can see that they are primarily pink shades featuring dainty floral prints and flowy, mini-length dresses.
To capitalise on the opportunity, Revolve or brands catering to the same consumers can look into stocking more floral print dresses with the above details.”
With the insights above, you can strategise your assortment planning to adapt to consumer demand. At the end of the day, the objective is to use data to lead your team in the right direction and reach that “aha” moment to make analytically informed decisions.
How to boost your data storytelling skills?
Understanding the best practices for excellent data storytelling is vital to ensure that the insights you have discovered are translated effectively to your audience. Here are four best practices to boost your data storytelling skills:
1. Consider your audience
By considering your audience, you can help make data consumable, not only to yourself but to your team by identifying the following:
- Your audience
- Their knowledge on the topic
- Their passions and their goals
Once that is established, you can start building your storyboard to suit your audience.
For example, suppose your audience is your directors. They are looking for higher-level findings and the next action steps. In that case, you might want to focus your story on presenting insightful and strategic data such as the best-selling color, patterns or categories. From there, you can dive deeper into what the brand should do to capture such opportunities.
2. Highlight the data that matters
Specific goals and audiences require particular data. That is why it is crucial to highlight the data that will help support your goals. For example, look back at your primary question and eliminate any unwanted data that might distract your audience from answering the question. This will highlight the data that matters, and therefore guide your audience’s attention to different opportunities shown in the numbers.
For example, if your goal is to find the right distribution channel to venture into, highlight how the top retailers differ in price, sell-out rate, and average discounts, and exclude other data that does not serve to answer this question.
3. Include the right visuals
Visuals, such as graphs, tables, and charts, allows your audience to grasp insights quicker than words. However, using the wrong visuals can sometimes mistranslate your findings and cause misunderstanding between you and your audience.
Luckily, most data analytic platforms already present data through visuals. So, once you have filtered your data, you don’t have to worry about creating a graph or a chart for your findings. Discerning when to use graphs or charts is another skill on its own. If you would like to learn more, read this article on how to choose the right chart or graph for your data.
4. Build a clear and engaging narrative
When it comes to storytelling, we are all familiar with the traditional arc of beginning, middle and end. Similarly, for data storytelling, the same arc is adapted into three acts.
The purpose of the first act is to set the context for your audiences, such as what market, brand, segment or timeline you are looking at. The second act confronts the problem, such as any missed opportunities that the brand is not taking advantage of. Finally, the last act concludes by suggesting the next action step that can resolve the issue.
Through this flow, your audience can understand the situation at hand, grasp the importance of the opportunity presented, and recognize what needs to be done to take advantage of it.
You don’t need to be an expert to be good at data storytelling. As long as you follow the best practices, you are able to translate data into actions that will allow your company to stay competitive.
Feel free to contact your Client Success Manager at email@example.com to work together on creating your own data story.