We are living in the information age and the world is inundated with data. This can be especially daunting for organizations, which are defined by their data. It is also the age of misinformation and essential that companies control how their data is interpreted and presented to their stakeholders, both internally and externally.
Enter data storytelling: the ability to translate data into narratives using visualizations and words. These stories ensure that organizations can focus on how their information is relayed. Data storytelling is essential to business and a key skill for the future.
A good case study for this is ESG: environmental, social, governance. As customers, employees, and investors are increasing their demand for social responsibility and sustainability in the companies they buy from, work for, and invest in, ESG reporting is increasing in influence and relevance. Data storytelling is essential for explaining in real time an organization’s evolving journey with ESG. We’ll come back to ESG as an example throughout this post, but first, let’s explore what we mean by data storytelling.
What is data storytelling?
Science has shown that information is processed by the language centers of the brain, which organizes words, numbers, facts, and figures into a story. The story the brain creates on its own, however, is often not the tale the information is meant to tell. If an organization—or friend, manager, or ad campaign—wants to assure that the information they are imparting is understood as intended, then they often need to develop a narrative.
Stories are an effective way of communicating because they:
- Strengthen the connection between the storyteller and listener
- Elicit an emotional response
- Enhance memory by helping the brain convert short-term memories into long-term memories
Data storytelling is the bridge that connects metrics with people. Data analysis is a “hard skill” that is essential to running a business. Transforming raw data into graphs and charts showcases the information in a format that can be assessed by specific employees and stakeholders. However, these assessments are subject to the interpretation of the observer. Whereas a narrative created through data storytelling allows everyone—regardless of where they fall on the data literacy spectrum—to easily understand the information as intended and, ideally, inspires a specific response or action.
Data storytelling is the bridge that connects metrics with people.
Data storytelling can be used internally (for instance, to communicate the need for product improvements based on user data) or externally (to create a case to customers to buy your product). Regardless of the audience, a good data-driven story requires three key components.
The three (sometimes four) components of a data-driven story
- Data: The first step in creating a powerful metrics-based story is processing and preparing statistics for analysis and communication.
- Narrative: The narrative (or storyline) is used to communicate insights extracted from data, its context, and recommended actions.
- Visualization: Visual representations of the data and narrative are essential for communicating the key message clearly and memorably. The adage “a picture is worth a thousand words” does apply here; but with graphs and charts, the words are still necessary to ensure the correct message.
In addition to these three elements, a fourth component is worth considering, especially when creating a single story meant for a wide audience:
- Language: To ensure that your story is understandable to any audience, consider using language that is simple and concise.
What isn’t data storytelling?
Data storytelling has evolved over time, but it is still misunderstood by many. The following are examples of how data storytelling has been misinterpreted or misconstrued. These are not data stories:
- A story supplemented by facts and figures: Instead, a data story is when analysis reveals a key insight and a narrative is built upon it to convey meaning and significance to others. Data is the foundation.
- Descriptive commentary: A chart or graph with commentary is not a data story. Commentary may help illustrate what the data is, but the narrative is essential to conveying insights and inspiring actions.
- Added context: As above, context enhances the comprehension of the data, but it still leaves the final interpretation up to the other party. For example, ESG depends not only on current data, but on past reporting and future direction to be effective. That requires more than context, it needs a narrative arc.
- Data charts: Obviously, if charts with commentary and context are not data stories, a chart alone isn’t either.
- Dashboards: Many organizations still use dashboards as the primary way to disseminate data to their employees. However, in a 2021 survey, 61% said dashboards were ineffective due to lack of context, 54% said they were ineffective because they were trying to communicate too much information, and 49% said they were ineffective because they were not able to tailor the information to share with stakeholders.
The connection between data storytelling and ESG
One thing is clear: Investing based on ESG reporting is growing at an exponential rate as consumers, employees, and stakeholders demand greater social responsibility from corporations. According to Forbes, ESG investing will grow another 21.5% by 2026, which represents $1 out of every $5 invested, and according to a study by Deloitte, 28% of consumers have stopped buying certain products due to ethical or environmental concerns. Hiring in ESG positions is also on the rise, with companies such as PWC reporting that they expect to add 100,000 new ESG jobs by 2026. So an organization’s ESG reporting and the stories it builds around that data can make or break its success in today’s world.
Data storytelling has become vital in every sector of business, however, there is a timely symbiosis between ESG and data storytelling. ESG aggregates all the data pertaining to organizational social responsibility while data storytelling allows companies to share and humanize that information with the world.
Consider the 2021 UKG ESG report: It focuses on numbers and stories. It looks at how UKG’s commitment to specific targeted goals like diversity, waste reduction, volunteerism, and business ethics improves the company and improves the lives of the employees, customers, and the communities where UKG does business. It inspires current employees to engage with purpose; it influences new talent to apply; it provides consumers with transparency on social responsibility; and it encourages investment. ESG needs data storytelling to craft a narrative that all stakeholders can understand.
How to tell a data story
What makes a data story different from other types of business storytelling? A data story is gleaned from statistics and will always include visualizations. The narrative, however, can be created using the same framework as any business story:
- Data insight: The insight gleaned from analysis that the story will be built around.
- Character: Who the story is about. In business, this will often be a demographic or generic character(s): consumer, supplier, stakeholders. It can also be you, an executive, the company, the brand, or an individual in an analogy.
- Setting: Where the story takes place. In business, the setting is more often what the story is about than a physical setting: marketing campaigns, the stock market, social media, product development. It can be set in a meeting, an interaction, or a physical locale.
- Conflict: The issue that has to be solved. In data storytelling, this will be the insight gleaned from analysis.
- Resolution: The action that will solve the conflict and the actions moving forward to support the resolution.
Example of an ESG data story:
- Data insight: While analyzing a data set, you discover a drop in sales for customers ages 18-27 coincides with a viral social media story about your company and water pollution.
- Character: Your characters are consumers in the 18-27 age group and environmentally conscious consumers. In this instance, the character is a demographic, not an individual.
- Setting: The scene you are setting is the drop in sales to 18–27-year-olds. Use data visualizations to illustrate this decline. Discuss the influence of social media on this age group and on conscious consumerism.
- Conflict: An accident at one of the organization’s factories caused water pollution. Explain how the viral social media post overshadowed the company’s handling of the accident, and how sales declined. Accompany this with appropriate visuals.
- Resolution: Craft a narrative for consumers and stakeholders regarding the history of the organization’s environmental responsibility. Use data pertaining to its positive ESG record and explain steps the organization is taking to prevent future accidents. Lastly, address the efforts the company is making to support the community, and help the environment during clean up. Explain this with accompanying visuals.
Skills needed to become a data storyteller
You don’t have to have a degree in data management, but you do need to improve your data literacy and build data analysis skills. Here is a list of helpful suggestions for becoming a data storyteller.
- Improve your data literacy. If you’re not a data analyst, you will want to improve your ability to analyze data for insights that can be the foundation for compelling data stories. Data analysts may be able to identify insights, but they’re less likely to be able to create a narrative.
- Know how to ask the right questions. For example: What is the source of the data? What problem needs to be solved? Who is the audience? Is this the right data for that audience?
- Learn how to identify implicit biases. In the case of data analysis, bias can come into play when you favor data that supports an objective or discount data which doesn’t. Bias can also be injected into the framework of a study from its inception. You must ask yourself: Were there biases in the parameters when the research study was created? Do you, personally, have implicit biases? Is your analysis of raw data biased toward supporting a preconceived objective? Today, we all need to be accountable for how we analyze data.
- Know how to find the true story within the data. Remember, data storytelling is not using data to support a story, it’s building a narrative from data. Identifying a correlation or causal link that runs through a particular data set can reveal an insight that helps construct a more interesting narrative.
- Learn how to determine what data matters. Once you have established how to approach your data without bias, first clear out data that doesn’t matter: duplicate data, errors or inconsistencies, and irrelevant data. Then look at the data that supports the insight and specific narrative. The narrative needs to be based in truth.
- Know how to choose the right data visualizations. Does your story need infographics, charts and graphs, timelines, maps, or a combination of graphics? How many graphics are needed? What is the best format? Remove the “clutter” from visuals. Do not include extra graphics or data that will confuse or distract from the narrative.
The takeaway: Organizations should prioritize data storytelling
Data storytellers are in high demand.
Organizations are listing storytelling as a required skill for data analysts when hiring, and in some cases, they’re creating positions specifically for data storytellers. To nurture data storytellers within your organization, encourage analysts and storytellers—visual and narrative—to collaborate and learn from each other. Teams made up of both analysts and storytellers will result in more compelling narratives based on deeper and more accurate insights.
Teams made up of both analysts and storytellers will result in more compelling narratives based on deeper and more accurate insights.
Organizations should also hire data analysts with storytelling skills, create storytelling positions, and build data literacy and storytelling skills for all employees, especially those in ESG and other data-driven departments.
The bottom line is that when an organization uses data to tell its own stories—rather than relying on others to interpret the data—the message will be clear, and the organization will benefit.