The Power of Data Storytelling: Know your Audience

When your data science team is composing a story, it needs to understand the data storytelling audience in order to connect with it on a personal and emotional level. I witnessed this myself when working for a large political campaign in 2000 and observed two candidates deliver their campaign speeches — one who connected with the audience on a personal and emotional level and another who didn't.

First, the candidate who didn't: He was speaking to a group of people who had lost many jobs over several decades. He stood in the red skeleton of an old industrial building and talked about his proposal for job training. He explained how job training would benefit members of the audience by equipping them with high-tech skills for the evolving economy, making use of big data to support his points. You could tell that the audience didn't connect with the story. When he was done, he received polite, scattered applause.

A few days later, the opposing candidate delivered his speech at an old abandoned warehouse next to a lazy brown river. The audience was the same — people who had lost many jobs over several decades. He started by saying, "I know many of you are uncertain. You're not sure that your way of life has a future." He then proceeded to tell a story about the importance of preserving what's important. At the end of the story, the warehouse exploded in applause that continued until the candidate stepped off the stage.

The first candidate didn't get it — he totally misread the audience. This group of voters didn't want to go back to school to learn a new trade; they wanted the world to go back to the way it was when they had good jobs and decent pay. The second candidate understood the audience and spoke to their desires and dreams. He was rewarded accordingly.

Classifying Audience Members: Unlock the Power of data

As a member of a data science team, you probably won't be speaking to a group of voters, but the same principle applies — to connect with an audience, you need to know the audience. Only by knowing the audience can you tailor your story to connect with audience members on an emotional level, where learning and transformation are most likely to occur, and data storytelling inspires such connections.

In most organizations, you can think of audience members belonging to one of the following five groups:

1. Observer: Observers have no stake in the story you're about to tell and are probably in the audience because attendance is mandatory or they have nothing better to do. You may not be able to do much to connect with observers, but by telling a compelling story and limiting your use of technical jargon and acronyms, you have a chance.
2. Gatekeeper: Unlike observers, gatekeepers have some skin in the game. They will be positively or negatively impacted by any recommendations the data science team proposes. Be aware of what the gatekeepers stand to lose or gain and tailor your message accordingly. Play up what they stand to gain, and play down what they stand to lose. If possible, make the connection to the gatekeeper's department explicit by using examples relevant to that department.
3. ManagerManagers may be gatekeepers, but in addition to wanting to know what they stand to gain or lose, they want to know the logistics — if a change is proposed, how will that change be implemented, especially concerning data privacy? How will it affect the way the department executes? How will it impact the workload? If you propose a change, you need to anticipate and address the concerns of everyone in the audience who may be impacted. Also keep in mind that managers are likely to ask the most questions.
4. Expert: Experts are most likely to challenge anything you say that crosses into their realm of expertise. If you haven't done your homework, an expert can completely derail your story and undermine your credibility. You would be wise to consult experts prior to delivering your presentation to ensure that you have the right facts and figures, are drawing reasonable conclusions, and have their buy-in. If you are met with a challenge, address the challenge and explain any new details or concepts you bring up, so the rest of the audience doesn't feel lost in the world of data.
5. Executive: The executives in your audience want answers to big questions, so be sure to end your story with a big reveal or call to action — something significant and relevant to the organization's success. It's always a good sign if, at the end of your story, the executive asks a question such as, "How do you see the impact of this on the rest of the organization?" Also, if any executive is likely to attend, avoid using too many slides. If they’re staring at your slides, they're not listening to your story.

Warming Up the Room: Telling a story with Data

While it's best to understand your audience prior preparing your story, that's not always possible. When presenting to an unfamiliar audience, you can get to know its members a little better through a technique called warming up the room. Five to ten minutes prior to the presentation, as people are settling into their seats, walk around and chitchat with some of the people in the audience. Some of them will tell you what they're looking for directly. You might hear comments such as, "I'm curious to see how this connects to what I'm working on." Then you can ask, "What are you working on?" If something like this happens, you might want to adapt your story in real time to meet your audience’s expectations, possibly using data storytelling to create a more engaging narrative.

The big takeaway here is to know your audience and anticipate and address their interests and concerns. Otherwise, the audience will merely hear what you say; they won't listen or retain it, and they certainly won't learn or be convinced to change in any way.

Frequently Asked Questions

What is data storytelling and why is it important?

Data storytelling is the process of transforming complex data and analysis into a compelling narrative that enables your audience to understand and act upon the information. It is important because it helps unlock the power of data, making it more accessible and engaging for a diverse audience, and aids in effective decision-making.

How can I use data visualization in data storytelling?

Data visualization is a key component of data storytelling. It involves presenting data visually through charts, graphs, and other visual formats, making complex data easier to understand, and allowing the audience to quickly grasp key insights and trends. Effective data visualization enhances your data narrative and helps engage the audience.

What elements are essential for an effective data story?

An effective data story should include clear data points, a compelling narrative, and appropriate data visualization. Additionally, it should be tailored to the target audience, making the data accessible and engaging, and should help the audience make informed decisions by presenting data insights in a coherent manner.

How can I tailor my data story to my audience?

To tailor your data story to your audience, you first need to understand who they are and what their needs are. Consider the data literacy level of your audience, their interests, and what decisions they need to make based on the data. This allows you to present your data story in a way that resonates with the audience and meets their specific needs.

Can you provide examples of data storytelling techniques?

Some effective data storytelling techniques include using relatable analogies to explain complex data, incorporating real-life examples or case studies, and focusing on data points that have a significant impact. Additionally, using storytelling techniques such as setting a context, building a plot, and introducing characters can make the data narrative more engaging and relatable to your audience.

What are the common challenges in data storytelling?

Common challenges in data storytelling include selecting the right data to present, ensuring the data is accurate and reliable, and balancing the complexity of the data with clarity in the narrative. It's also important to maintain the audience's interest and ensure that the data insights are actionable and relevant to their needs.

How does data storytelling help in making better decisions?

Data storytelling helps in making better decisions by presenting data insights in a clear and compelling manner. By using data storytelling, you can make the data more accessible and understandable, helping the audience see the significance of the data points and make informed decisions based on the data presented.

What skills are necessary for effective data storytelling?

Effective data storytelling requires a combination of data analysis skills, data visualization skills, and storytelling skills. You need to be able to analyze data, identify key insights, and present them in a visually appealing and engaging way. Additionally, you should be able to craft a compelling narrative that resonates with your audience and effectively communicates the data insights.

How can I improve my data storytelling skills?

To improve your data storytelling skills, practice is key. Work on analyzing different data sets, experiment with various data visualization tools, and create multiple data stories. Seek feedback from peers or mentors, learn from successful data storytelling examples, and continuously refine your ability to combine data analysis, visualization, and narrative elements.

What role does audience understanding play in data storytelling?

Understanding your audience is crucial for effective data storytelling. Knowing your audience's level of data literacy, interests, and decision-making needs allows you to tailor your data story to their specific requirements. This helps ensure that your data story resonates with the audience, making the data insights more compelling and actionable for them.

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 science. 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.?

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More Sources:

1. https://www.correlation-one.com/blog/data-storytelling
2. https://www.dhirubhai.net/advice/3/what-most-common-challenges-when-presenting-research-2qehe
3. https://online.jcu.edu.au/blog/the-importance-of-data-storytelling
4. https://mindfulpresenter.com/high-impact-presentation-skills-d-data/
5. https://www.prezent.ai/zenpedia/present-effective-presentations-to-senior-executives