The Challenges of Storytelling
Whether you’re working in data analytics or data science, the ability to turn your data into a story is an essential communication skill. Your insights are only valuable if you can communicate them effectively. Many professionals clearly see key trends in their data. They present their findings by repeating these insights as clearly as possible. They are then surprised when others fail to see the value in their work. The issue is not the quality of their insights, it is the lack of a story. We all process information a bit differently. Facts connect with some, and not others. Stories, however, are universal.
Data professionals do not need to be natural storytellers. Instead, they simply must understand the elements of a good story.
Great stories don’t simply communicate events and facts. They frame them in a way that engages the audience from start to finish. Great stories have 3 essential parts: characters, conflicts, and a conclusion.
I have a good friend who is terrible at telling stories. No matter how interesting the topic, his stories always seem to fall flat. He tends to focus on facts and is generally unable to create conflict or tension with relatable characters. Simply put, his stories are boring. When listening to him, I am rarely concerned about the conclusion. His stories end with a casual laugh and then someone quickly changes the topic.
My sister, however, is an amazing storyteller. She is able to make the most mundane events engaging. Her stories seem to connect with everyone. People laugh. Listeners pay attention. Others retell her stories. She combines characters, conflict, and a conclusion seamlessly. A good business story must do the same.
Characters + Conflict + Conclusion
First, every story needs characters. Data affects somebody. Identify these people. It could be stakeholders such as customers or employees. It could be the general public. Data-driven stories frequently offer the opportunity to make our audience members the key characters in the story. This approach can be highly effective when done properly.
Next, identify the conflict. It should relate directly to what the characters are experiencing. What problems must be solved? Why are these problems so important? How are these issues affecting their well-being? When our audience members are the characters we can often ask, what problem are they causing? These questions help storytellers find a narrative that resonates with their audience.
Finally, a good story must have a conclusion. A conflict needs resolution. A great data-driven story finds its resolution from the data. It is our secret weapon. It is the tool that allows us to face challenging conflicts confidently. This is where your insights into the situation become essential. As a data expert, you understand the problem and know how to solve it. You can restore balance by eliminating the conflict the characters are facing. This motivates your audience to act and provides a satisfying ending to the story.
Creating a Narrative
Let’s take a look at a simple example using demand forecasting at a shoe store. We could directly state the problem:
We do not have enough sizes or styles at the right times. Demand forecasting models can mitigate this problem.
This approach, however, lacks engagement.
So how do we engage our audience? We’ll want to use a hook during our introduction that introduces our conflict and characters. In our shoe store example, our conflict is the lack of stock. The characters could be customers, employees, or the organization as a whole.
Here’s a potential introduction:
I don’t buy shoes very often but they’re something I’m willing to spend money on. Last year, my beloved pair of Clarks desert boots reached the end of their life. It was unfortunate timing as they’re my go-to shoe and I had big weekend plans.
So I ran to the nearest “Shoes R Us.” I arrived and was immediately greeted by a helpful assistant named Paul. I told Paul what I was looking for: “brown Clark desert boots size 10.” He went to the back and returned empty-handed. I received the dreaded “Sorry we’re out of stock but we have….” I told Paul sorry but I’m not interested.
I knew I didn’t want any of the alternative options, so I left the store and was able to find what I wanted at a competitor down the street.
"Shoes R Us" lost out on a sale of a shoe that costs over $120.
As the customer, I lost out on time and had to reevaluate my loyalty to the company.
Paul lost out on commission.
Ultimately, this situation leads to decreased customer loyalty, employee turnover, and lost revenue.
Now, this problem isn’t unique. Given the vast stocks that we must have at every “Shoes R Us” location, it will certainly happen from time to time.
So how can we solve this issue?
Luckily, we can create machine learning models that will help us predict the demand for each style and size at all of our retail locations across the country.
Here’s how it works…….
After hooking your audience during an introduction, you’d then go on to explain the issue more in-depth. This is a more functional part of the story where you can introduce your methodology, data, and model. You can expand on the issue, highlight your knowledge, and offer ideas on what can be done to solve the problem.
Finally, it's important to end your story on a strong note. During your conclusion, you can finish the story by tying in what was discussed during your introduction. One of my favorite techniques is to highlight how things may be different for various stakeholders. Using our previous example, I could mention how customers like myself would have increased loyalty, how employees like Paul will make more money (and likely be happier in the process), and the company will see an increase in sales.
Your story has first highlighted the problem, you then showed how your knowledge can resolve the issue, and finished by describing how the situation will change. This can be incredibly motivating. It’s likely your audience wants this problem solved. The information you’ve presented is the bridge between the current problematic situation (which you highlighted during the introduction) and an ideal situation (which you introduced during your conclusion). You are giving them the power to eliminate this problem.
The Language of Storytelling
Once you understand the elements of your story, you’ll need certain language skills to bring it together. A story must have a structure. A clear beginning, middle, and end are essential.
Using discourse markers effectively will ensure your story flows. These language features let you transition to new points, recall important facts, or summarize the main idea. They are often the difference between an average story and a great story.
Depending on your audience, brevity and conciseness may be highly valued. Skilled storytellers are able to include characters, a conflict, and a conclusion into just a few sentences when necessary.
In other situations, great storytellers build suspense. If your audience is skeptical you may spend more time highlighting the problem. For example, you could elicit empathy for multiple characters to demonstrate the breadth of the problem.
Being able to craft an effective narrative for different audiences requires quite a bit of practice. The payoffs, however, are enormous.
Excellent Storytellers are Made, Not Born
The most successful data professionals combine world-class analytical abilities with expert communication skills. These individuals are well-known for their talent to turn insights into action.
How do they do it? Through stories. Storytelling is a do-it-all tool. It helps you simplify complex ideas, engage an audience, and motivate action.
So do your communication skills match your analytical skills?
Developing storytelling abilities in a foreign language, such as English, is challenging, but it’s a skill that can be developed by anyone. It will allow you to rapidly advance in the field. It opens doors to international opportunities, can lead to promotions, and makes you an invaluable member of your organization.
Are you looking to become an expert storyteller? Book your free consultation now.