Take Data and Add a Story
When communicated in the form of statistics, graphs and charts, data often requires effort to extract its value. Data storytelling, which relies on visuals, data and narratives, helps to convey insights in the form of meaningful stories that people can understand more easily and then translate into actions or business outcomes. Visuals reveal insights the audience wouldn’t see with just charts or graphs. When combined with the right narrative and context, data can become a story that not only entertains and engages but also influences and drives change.
Data and analytics-based stories are important for organizations. Analytics inform business stakeholders’ decisions, but understanding the details and meaning of these numbers can be time-consuming, boring and even difficult for numerically-challenged individuals. With data-infused stories, organizations can convince more than with stories based solely on anecdotes or personal experiences.
According to Stanford Professor and Social Psychologist Jennifer Aaker, “when data and stories are used together, they resonate with audiences both intellectually and emotionally”. Thus, data stories are also not only more effective in conveying memorable messages; they are also more emotionally persuasive.
Here are a few examples to illustrate what data story-telling looks like:
- One creative example of a brand leveraging data to create compelling stories is Google Trends, a web facility that offers daily graphs, charts, and stories about the information people are searching for around the world. Google even created a video for its ‘A Year in Search’ lookback, which added an emotional layer to the narrative.
- Bloomberg Business gathered all the public data they could about climate change and organized the data into a series of interactive charts under the title ‘What’s really warming the world?’ Readers could guide themselves through the story at their own pace and explore the question in step by step fashion.
- The Wall Street Journal created a series exploring the world in the year 2050, in order to help subscribers interpret and respond to economic indicators. The demographic data was presented in a sequence of handy visualizations and anecdotes, all of which helped readers understand the macroeconomic trends predicted to reshape the global marketplace.
Five Steps to Powerful Data Storytelling
While there are no set rules on how to tell compelling stories, the following basic guidelines and strategies can help brands create more interesting, dynamic and effective data-driven content.
1. Identify the audience.
The first step to telling a good story is to understand the audience, which helps define a successful narrative and relevant language. To convey a specific message that resonates with the recipients, brands must create a context to which they can relate with the level of information they have.
2. Find a compelling narrative
The next step is to figure out what story to tell from the data and analysis businesses want to share. The main goal should be to outline a brief, clear and compelling narrative that makes sense of the data, sets the direction for further analysis and encourages the audience to take some sort of action.
3. Be objective and uncensored
The purpose of telling stories with data is to increase the credibility of the content and validate the brand. Therefore, brands must be transparent if the audience is to trust the data source. If the data comes from a secondary source, it must be clarified who gathered the data and how, or the brand may risk losing trust and credibility.
4. Get the structure and flow right
Structure is important because it holds the story together. What makes data easily accessible to audiences is how well the information being conveyed is arranged. In order to make the data and information more accessible and easier to assimilate, the story should have a beginning, middle and end. The story should also have a hook, momentum and a captivating purpose.
5. Create supporting graphics
For a story to resonate with the audience, there needs to be compelling visuals. Choosing visuals that best tell the story and require the least amount of interpretation is an important part of the storytelling process. However, not all types of visualization are suitable for all types of data, so brands must determine the optimal data visualization for each of their findings.
The Future of Data Storytelling
Several trends are likely to shape the future of data storytelling moving forward but two have already started making their mark: automation and interactivity. Once a difficult and dreary process, data visualization is set to be significantly transformed by automation. Through the use of tools, applications and software that are becoming increasingly proficient at understanding data; entire databases and spreadsheets can now be automatically visualized on the run.
Interactivity, which has been a key feature of online data visualization over the past few years, has started to replace motionless visualizations as the primary way of presentation, allowing audiences to switch between different types of visualizations, select and explore their areas of interest and watch animations over time.
With the likely future of visualization being one of increasing interactivity and automation, data storytelling will only become more popular and audiences will soon look forward to being presented with information. Data will be a source of excitement and even entertainment. Companies would be well advised to become versed in this skill before their competitors do.