Datatelling: Turning your data into captivating stories

Welcome to the fourth and final episode of our data-focused series.

In previous articles, we explored the importance of structuring and organizing data to maximize operational efficiency (Data Management), the rules governing its use and the roles of various stakeholders (Data Governance), and how to turn raw data into actionable insights (Data Analysis). 

Did you know that a story is 22 times more memorable than raw data? Or that only 30% of employees say they are convinced by data-only presentations?

With Datatelling, we bring data to life by transforming numbers, graphs and tables into captivating stories, to convince, inspire and guide decision-making.

In this article, I propose to explore the basics of datatelling, its concrete benefits, the key steps to turn your data into data-driven stories and inspiring examples from the retail world.

What is Datatelling?

Datatelling, a blend of "data" and "storytelling", turns data into engaging and memorable stories, integrating narrative and context to highlight key messages. It goes beyond simple Data Visualization.

In short, data visualization makes data visible, while datatelling makes it meaningful and strategic. A clear message, an engaging narrative structure, and impactful visuals are the foundation of effective datatelling. 

It is not about manipulating, it ‘s about making data understood and revealing its full potential.

Now that we've defined Datatelling, let's look at why it is such a vital decision-making tool.

The essential role of datatelling

Stories are not just a nice way to present data, they multiply its impact. Studies show that stories can capture attention, create emotional connection, and most importantly, make complex data accessible and memorable.

Beyond memorization, datatelling helps to :

• Provide meaning through context.

• Spark emotion, reaching beyond pure logic.

• Simplify complexity to support decisions.

• Align teams around a shared vision.

Let's take a few examples.

• Showing that sales are up and down communicates information. Explaining the causes and offering solutions gives data life, it becomes a lever for action.

  
  

• A company’s leadership team expects dashboards with KPIs. But when these dashboards come with a note summarizing conclusions and providing recommendations or action plans, it facilitates decision-making—that’s what leaders expect from data professionals.

  
  

• In previous articles, I’ve often drawn from my experience at McDonald’s. I quickly realized that sending dashboards alone wasn’t enough. Sure, leadership could tell if the results were good or bad — but that didn’t provide the keys to future success.

  
  

Good datatelling is like a good book, it captures attention while synthesizing essential information, making it both understandable and memorable.

For a presentation, it’s more like a movie script, each act should engage the audience, emphasize key messages, and guide them toward concrete, actionable solutions.

In all cases, it starts with knowing your audience, adapting your message to the format, and telling a story where the most important insights are delivered, and next steps become clear.

There are three key steps to successful datatelling. Each one is essential to build its story.

Step 1: Understand your audience

Every story must be tailored to its audience. Ask yourself:

• What are their expectations? Managers want strategic recommendations; operational staff want practical solutions.

• What is their level of understanding? Simplify technical language for non-expert audiences.

  
  

➡️ Example in quick-service restaurant: A marketing manager will want to understand why a promotional campaign didn't work, while a regional manager will be more focused on its overall impact on revenue.

Step 2: Structure your story

Every story needs a clear and effective narrative framework:

Datatelling narrative structure

• Context / Initial situation – Set the scene: what data, what issue, why this analysis?

➡️ Example: Why did our kid's menu sales drop 15% this summer?

• Trigger / Problem – What changed? what’s the tension or questions?

➡️ Example: Did unusually cold weather change buying behavior?

• Development / Insights – Share findings, causes, analyses. Show how data illuminates the situation.

➡️ Example: A graph correlating temperature and kid’s menu sales.

• Outcome / Recommendations – What should be done? A clear, data-based, actionable conclusion.

➡️ Example: "We offer a targeted campaign with kid's games to regain market share."

Adapt the storyline to the format:

Once the narrative logic is in place, it must be adapted to the medium.

🎤 For oral presentation (meeting, pitch):

• Introduction – Set the context and stakes.

• Development – Present the facts, let the data speak, extract insights.

• Recommendations – Show the path forward.

• Conclusion – End strong with a clear message or call to action.

• 
  

📄 For a memo or report to executives:

• Context / Problem Statement – The « Why »: What’s the issue?

• Key Takeaways – What matters most, presented clearly and actionably.

• Recommendations – What needs to be decided or implemented.

• Detailed Analysis – The justification for recommendations.

• Methodology and sources – if needed, at the end.

• 
  

👉 This top-down approach, anchored in a real issue, helps leaders get to the point fast. They know why they’re reading, what they need to retain, and can explore further if they choose.

Step 3: Use the right tools and visualizations

Datatelling is based on impactful visualizations.

A few tips:

• Keep charts simple and avoid overly complex charts.

• Choose tools that match your needs and skill level (see my article on Data Analysis).

The concrete benefits of Datatelling:

Real-life examples

Example 1: A restaurant chain boosts dessert sales with datatelling

A restaurant network noticed big deviations in dessert sales performance. After a deep dive analysis, they identified that restaurants which implemented moderate price increases had significantly higher complementary dessert sales than those with more aggressive price hikes.By sharing these results through a structured narrative, the marketing team convinced management to offer an attractive dessert price to encourage supplementation.

Result: A strong increase in add-on dessert sales and a nearly 3% rise in total sales over three months.

Example 2: Fast Casual chain boosts customer engagement with consumer feedback

During a project, I used datatelling to present survey results on wait times. By telling a visual and engaging story, I helped the management team understand the emotional impact of delays on customer experience, which led to kitchen process optimization.

Result: Customer satisfaction rate increased by 5 points.

Datatelling pitfalls to avoid

Datatelling is an art and like any art, it takes pratice. Remember, you have to be convinced, be concise, precise and watch out for the following pitfalls:

• Lack of context: Data needs meaning. Connect it to goals.

• Too complex: Simplify to avoid losing your audience.

• Ignoring emotion: Stories that touch people are remembered.

• No call-to-action: Always guide your audience to clear recommendations.

In conclusion, Datatelling is not just a technique, it is a strategic lever. It helps mobilize your teams, inspire confidence among managers, franchisees and customers and make informed decisions.

As part of a data-driven strategy, datatelling is the final step in turning your data into impact.

Datatelling makes your data more accessible, understandable and engaging, transforming it into concrete actions that align teams around a common vision. This is where the magic happens.

So, why not start telling your own data-driven stories today to turn them into powerful performance drivers?

Need personalized support?

At TippsMe, we help you turn your data into tangible, measurable performance levers.

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