Data storytelling is extremely effective for communicating trends and vital insights from data. Unfortunately, most companies and even analysts aren’t very good at it.
Data is more than just numbers, tags, and categories. When presented in the right way, data tells a story.
For example, a construction company might keep detailed records of past projects. By analyzing these records and identifying trends, they can tell a story about how certain actions or variables impacted results. If they tell the story well enough, internal staff can make more accurate bids on future projects, and potential clients can see company value in hard numbers.
Crafting compelling stories based on data takes effort, however. Come along as we walk through the basics of data storytelling, why it’s important, and how you can do it effectively.
What is Data Storytelling?
Simply put, data storytelling is the art of breaking down data to highlight certain insights or developments.
As you might imagine, there are many ways to do this – which means there’s a lot to unpack. In addition to explaining the data, effective storytelling must also convey the data’s value and context. As a result, data-driven stories can range from simple graphs to infographics, storyboards, and company narratives.
But why go through the trouble of storytelling if you already get good insights from your analysts?
It’s not always so simple. Even the best insights are sometimes buried under piles of nuanced reports and different data management software. Data storytelling can help you dig data out of the digital weeds and give you clarity.
And added clarity isn’t the only reason data storytelling is crucial to modern companies.
Why is data storytelling so important?
Data storytelling has always been important — even before the age of computers and data management.
People have long used data storytelling to make sense of numbers, from annual revenue graphs to plotting trends in global health. These visual overviews demonstrate several reasons data storytelling is so important.
Makes data more accessible
Data isn’t always easy to decipher, even with the best data dashboards. As a result, data storytelling and visualization are essential for making data accessible to both technical and non-technical audiences alike.
Enhances competitive analysis
Competitive companies use data to keep ahead of the competition. Using storytelling to share internal trends and market data with key stakeholders allows everyone to see the same insights and develop new strategies.
Adds visual appeal
In a world numbed by countless PowerPoint presentations, attention-grabbing visuals are more important than ever. Mediums such as charts, graphs, and heatmaps are all great ways to share impactful data stories through visual communication — boosting internalization and retention.
Data storytelling also makes meetings, messages, and content more engaging. Nobody likes pouring through mundane reports or complex numbers. By crafting engaging data stories, you can share valuable data without putting people to sleep.
Gives value to data
Data is only as good as the insights it delivers. Even then, insights can only have an impact when they’re understood. With good data storytelling, you don’t just explain the data — you also show its true value.
What is data storytelling in business analytics?
Data storytelling and business analytics share a long history. After all, what would quarterly reports and profit projections be without engaging narratives and eye-catching visuals? Probably not very interesting.
In the context of business analytics, data storytelling is usually a combination of graphs, charts, and, in the best cases, narratives. Many business analytics platforms even come with built-in tools for generating these data stories.
As we’ll see in the next section, many central elements of data storytelling are commonly found in business analytics. Of course, these elements aren’t limited to business analytics — if anything, they’re universal to many areas.
What Are the Key Elements of Data Storytelling?
From eye-catching graphs to engaging reports, data stories come in many different forms.
While every data story is unique, each one shares three essential elements: data, narrative, and visuals. While that may seem like a no-brainer, considering these elements is crucial to drafting a complete data story.
You’ll find that each key element overlaps with the others in many ways. For example, narratives and visuals are one and the same for many data stories. As you explore each element, think about how they fit into the data story you want to tell.
At the heart of every data story is, of course, data. When trying to build a story, it should always be your first consideration.
Understanding your data is key to choosing your data story. Depending on the type of data and insights at your disposal, you may have one to several types of visuals and narratives to choose from.
However, poor quality data means inaccurate or even wildly incorrect insights and conclusions. So first, you need to cleanse your data, refine it, and choose the most relevant points. That’s not always an easy task, especially if you aren’t entirely sure what type of story you want to tell.
While there’s no right answer, the best strategy is to keep it simple. Instead of trying to tell several stories in one narrative, focus on one “big” trend or insight and go from there.
By identifying supporting data for only a specific area, you’ll keep your data (and your narrative) focused. Rinse and repeat until you have the points and supporting data you need for your story.
To make this feasible, you need to leverage data apps and data management tools. These resources can automatically refine and generate insights on their own. For example, this is what a data app built to visualize decarbonization numbers looks like in Toric:
Your data is the “bones,” the foundation of your data story. Where novels have fascinating characters in interesting scenarios, data stories have rich data framed in a relevant context. Also like a novel, spreading your story too thin across too much data can make it confusing.
Once you’ve gathered the right data and trends, the narrative and visuals will be easy to figure out. However, you may have multiple options to choose from.
The narrative of your story is what explains the data. More specifically, the narrative explains the trends and insights extracted from your data. To provide an accurate narrative, you may have to explain both the data and its trends.
Of course, what exactly you need to include (and how much you need to include) depends on the audience and the context of the story. As a result, it’s crucial to consider your audience when choosing a narrative.
For example, are they technical or non-technical? A technical audience may understand technical data without a breakdown, but a non-technical audience may need an additional explanation. Even a “clear” visualization of technical data may have no meaning to a non-technical audience. As a data storyteller, your job is to bridge this gap.
A major challenge in data storytelling is creating engaging narratives. Unfortunately, many applications of data visualization aren’t very interesting on their own — hence the need for it in the first place! While visuals can help here, they’re not always relevant, nor are they engaging enough on their own.
A compelling narrative relates data to a real problem or goal. In other words, why should your audience care? Your narrative shouldn't only explain your data but also how its insights can solve a particular problem or relate to a certain scenario.
For example, Facebook collects massive amounts of user data. While users might not care how many times they “liked” a page, they may find it interesting to know their “Top X” favorite posts. Facebook tells an effective story by relating raw data (number of likes) to something relatable (their own posts).
Where narratives explain things through text or speech, visuals break down the data visually and engage the audience. Many data stories are highly visual, so graphs and other visuals often provide a big part of the narrative. Creating a visual narrative isn’t easy, but it’s much more straightforward if you choose the right data.
Selecting relevant data identifies clear trends, which helps create clear narratives. In the right format, visualizing these trends should allow you to explain (or at least clarify) the data without words.
In other words, your visuals should clearly explain your data. Common examples include a linear trend on a graph or “hot points” on a heatmap.
Of course, different data calls for different formats. Selecting the right one to convey trends is known as data visualization. Storytelling and visualization have many similarities, but visualization hyper-focuses on translating data into visual mediums.
Visuals don’t just make data engaging — they also make it enlightening. Even if your audience understands the data and its context, seeing it in a visual form makes everything much clearer.
Thankfully, you don’t need to be an artist to create great visuals. With no-code data apps and tools, you can select relevant data, identify trends, and generate visuals at the click of a button. With the right settings, your regular data dashboard can be the perfect data visualization for weekly meetings.
How Can I Become Good at Data Storytelling?
So what makes a “good” data story?
It depends on several factors. Some data might require a graph or chart to explain, but others might benefit from a written narrative. Plus, no matter how good your data, narrative, or visuals, the story you tell must be relevant to your audience.
While there’s no one way to make a good data story, following this procedure should help. Consider these steps when drafting your next data story.
1. Consider context and expectations.
Why are you telling a data story in the first place? It may seem like an obvious question, but understanding the context and expectations of your story is a great place to start. Knowing your story’s exact purpose allows you to select the right data, draft relevant narratives, and create appropriate visuals.
2. Identify the most relevant data.
As an extension of the first point, be sure to use data and trends that are relevant to the purpose of your data story. While you may have plenty of interesting data to work with, it’s important to focus on key data points that illustrate your narrative — you also need to consider how you categorize and break down this data.
3. Choose the right narrative structure.
How will you tell your data story? You may have several options to choose from and even several good options among them. Above all, make sure your narrative structure and visuals support the trends you’re trying to convey. Of course, these should also be accessible to your intended audience.
4. Simplify your story.
It’s easy to go overboard, especially if you have a lot of data. If you find yourself with a complex, multi-part data story, try to take a step back. Rather than spreading your narrative too thin across too many insights, focus on only the most relevant data. While other data might support your narrative, it’s not worth including it if it makes your narrative confusing or unclear.
5. Make it matter to your audience.
Above all, your data story needs to matter to your audience. Even the best data story will fall flat if it doesn’t meet the expectations and goals of key stakeholders. While all the steps above can help make a relevant data story, it may help to ask yourself, “why does ‘x’ matter?” at every point in the process.
Tell Your Data Story with Toric
There’s definitely a lot to data storytelling.
However, with narratives and visuals deeply rooted in insights, telling a good data story requires good data management. With no-code data tools from Toric, you and your team can locate relevant data, identify key trends, and generate visuals without writing a single line of code.
If you want to start telling powerful stories with data, speak with a data management expert from Toric today.