What is data storytelling and what are its 3 main principles

Mastering the art of data storytelling can significantly enhance the impact of your presentations

Data alone is not enough: to best communicate the evidence gained from data analysis, emotional involvement is also required. This is why data storytelling was born, which combines the power of stories with the concreteness of data to invite action.

Data storytelling means, literally, ‘telling a story through data‘. That is, using the insights gained from data analysis to best communicate a message, thus inspiring one or more actions in the recipients.

The data contained in dashboards and spreadsheets take a snapshot of a situation but always fail to explain the relationships or cause-and-effect relationships. What is needed, therefore, is a narrative, an interpretation frame that links past, present and future for the benefit of all stakeholders, both corporate and territorial.

Easier said than done?

Perhaps, but in this article I will offer you 3 valuable tips to help you realise this goal and turn your data into a compelling narrative capable of engaging your audience and persuading them.

At the end of the reading you will have new tools at your disposal for truly effective presentations.

And if you would like to learn even more about this powerful technique, I invite you to have a look at my book ‘Presenting Data‘: there you will find everything you need to know to master data storytelling to perfection.

Read also: How to involve the audience during your presentations

Tailoring data narratives to match the audience: know your public!

Understanding the audience is the cornerstone of effective data storytelling. Without a deep comprehension of who your audience is, what motivates them, and how they make decisions, your data story is unlikely to achieve its intended impact.

But how do you go about truly understanding your audience, and why is it so critical in the context of data storytelling?

First, consider the importance of knowing your audience’s background and context.

Are they experts in the field or newcomers? What level of detail do they require?

Understanding their level of expertise helps you tailor your narrative to be neither too simplistic nor overly complex. For instance, presenting detailed statistical analyses to an audience unfamiliar with data science could lead to confusion rather than clarity. Conversely, oversimplifying information for a knowledgeable audience might come across as condescending.

By aligning the complexity of your data presentation with your audience’s familiarity with the subject, you can ensure that your message is both accessible and engaging.

Next, delve into the decision-making mechanisms of your audience.

What drives their choices? Are they influenced by quantitative data, or do they respond better to qualitative insights?

If your audience is driven by numerical evidence, emphasizing key statistics and trends can be effective. On the other hand, if they value personal stories and qualitative data, incorporating anecdotes and case studies can make your data more relatable and compelling.

If you know your audience well, you can easily select the information to be emphasised during the presentation. In data storytelling, after all, it is not important how much data you show the audience: it is important to understand which of these you present and which you do not.

Managing the transition from exploratory to explanatory phases

The success of your presentation will largely depend on how well you manage the transition from the exploratory to the explanatory phase.

“Maurizio, what are these two phases, and why is this transition so important?”.

In the exploratory phase, your primary goal is to delve into the data, uncover patterns, and generate insights. You might create numerous charts, graphs, and dashboards to help you make sense of the data and this phase is characterized by a high level of experimentation and analysis, as you seek to understand what they reveal.

However, when it comes time to communicate your findings, you must shift from exploration to explanation.

The explanatory phase involves distilling the most significant insights from your exploratory work and presenting them in a clear, coherent narrative. This is where the art of storytelling comes into play.

You can’t simply present all the exploratory graphs and expect your audience to draw meaningful conclusions on their own. Instead, you need to guide them through the data, highlighting the key points and explaining their significance.

Consider this: do you really think that a presentation full of countless graphs and diagrams, each one more complex than the last, is the right solution to engage your audience? That would be the best way to lose the audience’s attention, compromising the possibility of conveying the message you wish to communicate.

During the presentation you must avoid this by focusing on clarity and purpose. Each visual element in your slides should serve a specific function within the narrative. This means selecting the most relevant data points and visuals that best support your story.

I know, It’s natural to want to display the full extent of your hard work and the depth of your analysis. However, it’s a temptation that must be resisted. Your audience is not interested in the exhaustive details of your process. They care about the findings that are relevant to their needs and expectations.

It’s not about showcasing the breadth of your analysis but about communicating the insights that matter most to your audience. It’s about “connecting the dots” and combining the individual data points in order to support your message.

Read also: 3 ways to interact with the audience during a presentation and gain their interest

Anticipate your audience’s questions and objections!

In any presentation, your audience is likely to have questions, doubts, or concerns about the data and the conclusions you draw from it. Addressing these potential objections proactively can significantly enhance the credibility and persuasiveness of your narrative.

“But Maurizio, how can I anticipate these objections before they are even made?”

One effective strategy is to conduct a thorough review of your data and narrative before presenting.

Ask yourself critical questions:

• Have I clearly explained my data sources and methodologies?
• Are there any potential biases or limitations in the data that I need to acknowledge?
• What counterarguments might be raised, and how can I effectively refute them?

This self-interrogation process helps you build a more robust and defensible data story.

Another practical approach is to engage in brainstorming sessions with colleagues or peers.

You don’t necessarily have to imagine all the questions you might be asked: have your colleagues suggest them to you!

By discussing your presentation with others, you can gain valuable insights into potential weaknesses and areas of confusion, and identify and address objections that you might not have considered on your own. Additionally, feedback from others can provide fresh perspectives and enhance the overall quality of your data story.

You have no one to ask for feedback on possible questions and time is running out?

AI tools like Copilot can be immensely helpful in this regard.

They can simulate audience reactions, providing you with a list of potential questions and objections based on your presentation, so you can refine your narrative and ensure that you are well-prepared to address any challenges that might arise. For instance, if Copilot suggests that your audience might question the reliability of a particular data source, you can include additional information to validate the source and bolster your credibility.

Moreover, Copilot can help you in many other steps of the process of creating effective presentations. In practically every step!

Want to know how?

Read this mini-guide to using Copilot for PowerPoint presentations, full of step-by-step video tutorials explaining how to make the most of it to help you in your work.

Takeaways

• Data alone is not enough to inspire action. Combine the power of stories with the concreteness of data to create an emotionally engaging narrative. This approach not only makes your data more relatable but also more persuasive.

• Knowing who your audience is, their level of expertise, and what drives their decision-making is crucial. Tailoring the complexity and focus of your data presentation to match their background ensures your message is both accessible and engaging.

• The transition from exploring data to explaining it is vital. Focus on clarity and purpose by selecting the most relevant insights and visuals. Avoid overwhelming your audience with too many details; instead, guide them through the key points that support your narrative.

• Be proactive in addressing potential questions and concerns. Conduct a thorough review of your data and narrative, seek feedback from colleagues, and consider using AI tools like Copilot to simulate audience reactions and prepare for possible challenges.

• Data storytelling is about connecting individual data points to support your message. Create a coherent narrative that links past, present, and future, making it easier for your audience to understand the significance of your findings.

FAQs

What is the purpose of Data Storytelling?

Data storytelling combines the power of stories with the concreteness of data to communicate a message and inspire actions. It involves using insights from data analysis to create a compelling narrative that engages the audience.

Why is it important to know your audience before a presentation?

Knowing your audience is crucial for effective data storytelling. Understanding their background, expertise, and decision-making mechanisms allows you to tailor the complexity and focus of your data presentation, ensuring it is accessible and engaging.

How to anticipate audience questions during a presentation?

Anticipating audience questions involves reviewing your data and narrative beforehand. Consider potential biases, counterarguments, and engage in brainstorming sessions with colleagues or use AI tools like Copilot to identify and address possible objections.