To be a truly data-driven enterprise, organizations today need to go beyond just analyzing data. Instead, business experts and IT leaders need to turn relevant data into compelling stories that key stakeholders can easily understand — and use to make better business decisions.
Known as data storytelling, this essential skill is a key enabler for organizations that want to extract actionable information from their data, without getting lost in the sea of graphs and numbers typical of traditional data reporting.
Below is a look at what data storytelling is all about and how IT and analytics leaders can use it to realize the decision-making potential of data.
What is data storytelling?
Data storytelling is a method of conveying data-driven insights using stories and visualizations that engage audiences and help them better understand key conclusions and trends.
But that is often easier said than done.
“Telling stories with data can be difficult,” said Kathy Rudy, chief data and analytics officer at global technology research and consulting firm ISG.
For Rudy, telling data starts with knowing your audience.
“Don’t forget to start with who your main characters are, that is, the audience for your data story. What information is most important to them? Structure your data story so that you anticipate the next question the audience will have by thinking like the reader of the story,” says Rudy, adding that in her 20 years in benchmarking and data analytics, she has had to learn to tell a clear and clear story. concise narrative using data to validate ISG recommendations.
The first hurdle most data storytellers face is gaining acceptance for the validity of the data they present, she says. The best way to do this is to conduct data validation and understanding sessions to address the issue of data validity.
The goal of the data narrator is to clear up any questions about the source of the data, the age of the data, and so on so that the storyteller doesn’t constantly defend the data in later views of the data, Rudy says. say.
“Don’t be too technical or you’ll lose the audience,” she advises. “In the case of IT benchmarking, they don’t want to know anything about the technology stack, just that the data is relevant, secure, current, comparable and accurate.”
Elements of data storytelling
Data storytelling is made up of data visualization, story and context, said Peter Krensky, director and analyst of the business analytics and data science team at Gartner.
“With visualization, a picture is worth a thousand words,” he says. “How do you make the story visually appealing? Do you use a graphic or iconography? That doesn’t mean it can’t be a table or very dry information, but you’d better have a visual component.”
The story is the story itself – the who, what, where, why. It’s the emotional arc, Krensky says. “When it comes to sales forecasts for the quarter, are we doing well or are people going to lose their jobs?”
Context is what the people hearing this story need to know. Why one sales rep always outperforms all other salespeople exemplifies the context for a data story, Krensky says.
Grace Lee, chief data and analytics officer at The Bank of Nova Scotia (known as Scotiabank), says combining context and story requires an understanding of what makes a story compelling.
“The way we think about stories, if we remove the data term, it needs a plot that you care about, it needs characters that you fight for, and it requires a destiny or an outcome that you believe in and strive for” , she says.
Putting the data into context in the form of a story helps people care more about it and understand the action that comes out of it, Lee says. In addition to focusing on storytelling as a discipline, Lee’s team is also working to create more storytellers across the organization.
“The way we educate people about storytelling is really about action orientation, helping people create those stories, providing more of the context and showing people the clear line between the data, the insight and the action that needs to be taken. come,” she says.
Lee sees the role of Scotiabank’s data and analytics organization as the storyteller for the enterprise because only in the data is some of the insights about what customers need and want appear.
Key steps in telling data
Lars Sudmann, owner of Sudmann & Co., a Belgium-based network for consulting and management training, provides insight into the steps involved in data storytelling.
- Identify the “aha” insights: One of the biggest pitfalls of data-based presentations is the ‘data dump’. Rather than overwhelming the public with data and visualizations, CIOs and data analytics officers should identify and focus on one to three key “aha” insights from the data. What are the surprising, absolutely important things you need to know? Identify them and build your presentation around them, Sudmann says.
- Share the origin story of the data: To tell a good story with data, a good starting point is the genesis, or the origin of the data. Where is it from? This is especially important when narrators present data sets for the first time.
- Transform surprising turning points into captivating transitions: When storytellers present data and facts, they need to share where the data/graphs/trendlines make ‘surprising’ moves. Is there a jump? Is there a turning point? This can make for convincing transitions to deeper analysis, for example, “Normally we would think the data is doing X, but here we see it has decreased. Let’s see why this happened.”
- Develop your data: One of the biggest problems with giving presentations is people throwing heavy data on the screen and then playing catch-up with words like, “This is a busy slide, but let me explain.” “This may be difficult, but…” Instead, storytellers should develop their data step by step. “I’m not a fan of fancy animations, but in PowerPoint, for example, there’s one animation I recommend: the ‘appear’ animation,” says Sudmann. “This allows people to coordinate what they see and say, so that a data story can be built up step by step.”
- Highlight and highlight to bring your story to life: Once storytellers have identified the flow and key aspects of their data stories, it is important to highlight and emphasize the key points with their voice and body language. Show the data, point to it on the screen, walk up to it, circle it — then it comes to life, Sudmann says.
- Have a ‘hero’ and a ‘villain’: To make stories more compelling, data storytellers should also consider developing a hero, e.g., the “good tickets,” and a villain, e.g., “the bad tickets generated because they didn’t read the FAQs,” and then track their development in the over time, in different departments, as well as the “hero’s journey” to success, Sudmann advises.
Tips for telling data for success
Rudy firmly believes that the data should unfold through storytelling so that when the narrator finally hits the punch or the “so what, do what” their message is completely aligned.
As such, storytellers should start at the top and set the stage with the ‘what’. For example, in the case of an IT benchmark, the narrator might start by saying that the total IT spend is $X million per year (remember, the data has already been validated, so everyone nods).
The narrator then has to break it down into five categories: people, hardware, software, services, other (more kinks), Rudy says. Then divide it further into these technology areas: cloud, security, data center, network, and so on (more kinks).
Next, the narrator reveals that based on the company’s current usage volume, the unit cost is $X for each area of technology and explains that compared to competitors of similar size and complexity, the narrator’s organization spends more in certain areas, e.g. security (now everyone is really paying attention), says Rudy.
“You’ve just led your audience to the ‘so what’ part of the story, which is that there are areas for improvement,” she says. “The next question in your audience’s mind is probably, ‘Why?’ And finally, ‘So what do we do about it?’”
The rest of the story uses a common understanding of the validity of the data to make recommendations for change and the actions needed to make those changes, Rudy said. The data in this story created the credibility needed to make a call to armaments, a reason to change that is indisputable.
And with the old saying “if a tree falls in a forest and no one is around to hear it, does it make a sound?” In consideration, it is critical for data storytellers to consider the medium different individuals use to consume information and the times at which they can access this information.
“The pandemic has certainly helped drive the shift away from allowing thought workers to work from home,” said Kim Herrington, senior analyst for data leadership, organization and culture at Forrester Research. “And often you communicate with thinkers all over the world. So it’s important to think about the communication software you use and the communication standards you have with your team.”