Make Data Storytelling Pop!

In several previous newsletters I cover the essential elements of a data science story, but storytelling goes beyond the story itself. It's the telling of the story that separates a gripping story from one that is merely an accurate accounting of events. To make data storytelling pop, you need to believe it and be passionate about it. Only then will you be able to transfer that passion and conviction to your audience. In short, you need to sell it.

Take a Lesson from Law School

When I was a law school, I took a course on litigation. I learned that part of being a successful lawyer is the ability to make the jury empathize with your client. A jury would always want to know the backstory. They want to how the plaintiff and defendant arrived at this point, what made the plaintiff file suit, what did the defendant do and why?

The professor, who had several years' of jury trials behind him, offered some common-sense advice. He said say what you believe and say it with clarity and passion. He warned against trying to make ordinary stories extraordinary because what you know is the only account you can truthfully represent. Making up a far-fetched story about what happened is only likely to undermine your credibility.

The same holds true when you're telling a data science story. Don't try to fake it or feign interest in a topic or issue that is no interest to you. The audience will quickly pick up on any insincerity, and at that point, your credibility is shot.

Take a Lesson from Sales

If you've ever been on the receiving end of a good sales pitch, you know the secret ingredients. A salesperson who loves what they do and truly believes that the product would significantly improve your life in some way. It's almost as if the salesperson would buy one for you, if she could afford it, just so you could experience its benefits for yourself. With a good sales pitch, you can hear the passion and conviction in the person's voice and witness it in the person's body language.

On the other hand, if you've ever been on the receiving end of a lousy sales pitch, you probably could feel that you were being oversold. That the salesperson was overselling the product and was motivated by profit, not by a commitment to serve your best interests. Or maybe you felt that the person hated her job or was reluctant to sell this particular product; in other words, the salesperson wasn't sold on it herself.

When a data science team lacks conviction, it often becomes apparent in their use of data visualizations. Instead of telling a convincing story and backing it up with visualizations, the visualizations become a distraction to draw attention from the fact that the story really isn't all that interesting. The team thinks that by dangling a little eye candy in front of the audience, they won't notice that the team has nothing important to say.

Tips for Making Your Story Pop

Following are a few suggestions for presenting a story in an interesting way:

Choose an interesting topic. If the data science team has nothing interesting to present, it should be looking for something interesting, not wasting its time delivering a progress report. As Charles Caleb Colton once advised, "When you have nothing to say, say nothing."
Connect yourself to the story. Tell your audience why your team found the discovery fascinating. This provides you with an opportunity to share the team's passion.
Sound like a real person. Don't place professionalism over passion. In many organizations, employees are expected to "act professional," meaning they need to muffle their passion and humor. However, being professional is counterproductive to storytelling. The audience will connect better with someone who's excited and doesn't take himself too seriously.

Remember that you are the most important part of your presentation. Beautiful charts, clever anecdotes, and piles of data won’t make up for a lack of passion, humor, and grace. Even the most extraordinary data will seem boring if you can't tell it in an interesting way. The key is to make sure that you believe that the story is interesting. If you can’t convince yourself, you won't convince an audience.

Frequently Asked Questions

What is data storytelling?

Data storytelling means showing data in a way that's easy to understand. You use charts and graphs. You also tell a story with the data. This helps people get the point. It mixes looking at data with telling a good story. The goal is to make people care and take action.

Why is data storytelling important for engaging an audience?

Data storytelling matters because it lets you show data in a way that fits what people care about. You turn raw data into a story. This makes the data grab people's attention. It also makes the data more powerful and easier to understand.

How does data visualization contribute to data storytelling?

Data visualization is a key part of data storytelling. It translates data into visual formats like charts, graphs, and infographics. This visualization makes the data easier to understand and more interesting.

Good data visualization helps the audience quickly understand complex data. They can then act based on what they learn.

What are the key elements of successful data storytelling?

The key elements of successful data storytelling include having compelling data, clear objectives, a structured narrative, and effective data visualization.

It's also important to understand the audience and tailor the data story to their needs, ensuring that it inspires action and communicates data insights powerfully.

Who can benefit from using data storytelling?

Data storytelling can benefit anyone who needs to communicate data insights effectively. This includes data analysts, data scientists, business leaders, marketers, and anyone involved in analytics. Using storytelling and visualization, these stakeholders can make the data come alive, helping them to make informed decisions and take action.

How do you identify the right data for storytelling?

Identifying the right data involves understanding the objective of the story and the needs of the audience. It's important to focus on data points that are relevant, accurate, and compelling. Data quality is crucial, so use data from reliable sources and ensure it aligns with the story you want to tell.

Can you give an example of how data storytelling has been used in a real-world situation?

One example of data storytelling in a real-world situation is a company using data to improve customer satisfaction. By analyzing customer feedback and usage data, they identified key pain points, visualized the data using dashboards and infographics, and created a compelling story that motivated actionable changes. This data-driven approach led to significant improvements in customer experience by identifying data patterns and trends.

What are some best practices for data storytelling?

Best practices for data storytelling include starting with a clear objective, understanding your audience, using effective data visualization techniques, and creating a structured narrative. It's also important to focus on the most impactful data points, ensure data quality, and continuously refine your story based on feedback and new data insights.

How can one improve their skills in data storytelling?

Improving skills in data storytelling involves practice and learning from experts in the field. Participate in workshops, read relevant case studies, and study best practices in data visualization and storytelling. Additionally, work on real-world projects and seek feedback to continuously refine your approach. Joining communities of data storytellers can also provide valuable insights and inspiration.

This is my weekly newsletter that I call The Deep End because I want to go deeper than results you’ll see from searches or AI, incorporating insights from the history of data and data. Each week I’ll go deep to explain a topic that’s relevant to people who work with technology. I’ll be posting about artificial intelligence, data science, and data ethics.

This newsletter is 100% human written 💪 (* aside from a quick run through grammar and spell check).