Data is often presented in a no-nonsense manner, providing lines of text (and graphical information in some instances) that provide factual information based on thorough research and studies. But while this no-nonsense approach has its advantages, the downside is that data is presented in what can be described as “uninteresting,” often leaving its audience confused and unable to appreciate the information presented.
If a business aims to become a data-driven enterprise, it is crucial that the data it presents will be comprehensible and appreciated by its audience. One way to ensure these goals are met is to create compelling stories built around this data.
This is known as data storytelling, which conveys data-driven insights using narratives and visualizations that engage audiences and help them better understand key conclusions and trends. The objective is to create greater engagement with and understanding of the data so the organization can make sound decisions and policies based on such data.
The elements of data storytelling
Data storytelling is comprised of three key elements:
Data visualization – these are the graphical and other visual elements that illustrate the information of the data
Narrative – the who, what, where, and why found in the data
Context – the premise behind the data
Narrative and context are the more crucial elements in the data as they should blend together in order to be able to tell a cohesive and comprehensible story with the data.
“The way that we think about stories, if we remove the data term, it needs a plot that you care about, it needs characters that you root for, and it requires a destination or an outcome that you believe in and aspires to,” shares Grace Lee, chief data and analytics officer at The Bank of Nova Scotia as quoted in the CIO.
As such, being able to put the data into context in the form of a narrative allows people to care more and to understand what the action is that comes out of it.
The challenges of data storytelling
Data storytelling is not as simple or straightforward as it seems. Especially if one is unable to present a clear and cohesive narrative and context to the data.
Another challenge is that some presenters have difficulty relating the data to their audience. And that is because there is a disconnect between the data and how the audience could relate to it.
Kathy Rudy, chief data and analytics officer at global technology research and advisory firm ISG, had this to say, “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 you anticipate the next question the audience will have by thinking like the reader of the story.”
It’s also important to consider the medium various individuals are using to consume information and what times they’re accessing this information.
The third challenge is gaining acceptance for the validity of the data that is being presented. Thus, it is important to hold data validation sessions to get the question of data validity out of the way and address any questions with regard to the data.
Tips in data storytelling
Identify the ‘aha’ insights - Identify them and build the presentation around the surprising key insights the audience should know
Share the genesis story of the data - Where does it come from? This is especially important when storytellers present data sets for the first time.
Transform surprising turning points into engaging transitions
Develop the data - Avoid heavy data on the screen and then play “catch-up,”. Instead, build the data step-by-step.
Emphasize and highlight - Emphasize and highlight key points through voice, action, and body language.
Have a ‘hero’ and a ‘villain’ - Data storytellers should also consider developing a hero, e.g., the “good samples,” and a villain, e.g., “the bad samples,” and show their story like how it came to be and how it developed over time.
Letting the data unfold
The most important task for a data storyteller is to tell a story that will allow the data to unfold in such a way that when it finally is at the punch line or the “so what, do what” there is full alignment on their message.
As such, storytellers set the stage from the beginning, establishing the premise beforehand and breaking it and the data down into identifiable segments or elements that will create the structure of the story from the foundation already established.
“You have thus led your audience to the ‘so what part of the story, namely, that there are areas for improvement,” Ruby shared. “The next question in your audience’s mind is most likely, ‘Why?’ And finally, ‘So what do we do about it?'”
The rest of the story leverages a common understanding of the validity of the data thanks to the elements already established, thus the data already has the credibility necessary to establish an indisputable course of action. Source: https://www.cio.com/article/408345/data-storytelling-a-key-skill-for-data-driven-decision-making.html