“A story without data is just an opinion,” observed the legendary statistician W.Edward Deming—highlighting the importance of data storytelling in business.

Yet many professionals still struggle to apply it in practice, despite the concept being around for years. While a few surveys hint at this gap, my perspective is shaped primarily by conversations with tech leaders during my workshops.

In examining the gaps, I narrowed it down to a few core issues:

• Much of the literature on data storytelling—TED Talks, books, articles, videos—leans heavily toward the data side: charts, visualizations, and tools. The storytelling aspect often gets minimal attention, which limits professionals’ ability to apply it meaningfully at work.
• Many treat data storytelling as a single skill, when in reality it’s a blend of multiple skills that must work together.
• Most data visualization product firms claim data/chart descriptions as “data stories” —an oversimplification that masks the full picture.
• The common examples used in books and articles—Hans Rosling’s TED Talk, Napoleon Bonaparte’s march on Moscow, Florence Nightingale’s charts—rarely reflect the kind of data or problems we face in everyday business.

At StoryEQ, we cut through this confusion by helping participants see one simple truth:

“There is really no data storytelling. Only good storytelling, supported by data.”

So much so, we spend no more than 15–20 minutes on this topic in our workshop. Yet participants leave with a clear understanding of the concept and how to apply it.

In this article, I’ll unpack the logic behind the above statement to deepen your understanding.

First, I’ll sketch a simple story framework using movies, then translate that to business data. Along the way, I’ll also highlight some common blind spots technical professionals should watch for when adopting data storytelling.

Let’s Talk About Movies

There are many elements that make a movie compelling. But to draw a clear parallel to data storytelling, we’ll consider just four key attributes.

An enjoyable movie typically includes:

• A dire problem that disrupts the protagonist’s world and creates tension
• The insight the character gains by getting to the bottom of the problem
• An acceptable solutionthat brings meaningful closure
• Visualsthat support the story—helping the audience see and feel it for themselves

Consider the movie The Matrix.

Exposed to the idea of the Matrix by Morpheus, a skeptical Neo begins to question his reality and seeks the truth. His journey is turbulent and nearly fatal—until he discovers for himself that the world is a simulation. In the end, by realizing that he is the One as said in the prophecy, he leads the fight against the machines.

To bring this eerie concept to life, the movie is set in a dystopian future where intelligent machines control human lives. Striking visuals—monochrome code streams, sprawling machine cities, and the bleak, post-apocalyptic world—make the setting feel vivid and believable.

Consider Good Will Hunting.

A young janitor at MIT with an extraordinary gift for mathematics, Will is limited by his emotional trauma and fear of vulnerability. With the help of his therapist Sean, Will undergoes a rough path to recovery—until he realizes that all along he has been blaming himself for his lack of self-worth. Will makes decisions that allow him to lead a happy life.

What brings this story to life is its setting—MIT and Boston—an intellectual hub that makes Will’s journey plausible. In another city, this story might have fallen flat.

 

So, What About Data Storytelling?

Now, let’s take those same four components and apply them to your work situation.

1. A business problem worth solving: You encounter an anomaly or a problem in the data. It might show up during routine exploratory data analysis (EDA), while testing a hypothesis, or even by chance. What makes it worth pursuing is its potential impact—on revenue, customer churn, operational risk, or something else that matters to the business.

2. The insight at the bottom of it:You dig deeper. Sometimes the answer is obvious; more often, it isn’t. You explore multiple angles, test assumptions, and keep refining your approach until you find the root cause. This insight helps you determine the best course of action.

3. A solution that brings closure: Next, you figure out what can be done. The solution might involve changes to process, business, technology, or a combination of these. It could be a small change or a complex one. Other times, you might offer options with trade-offs.

4. Visuals that support your narrative: You use charts, dashboards, or models to bring your story to life, allowing the audience to see for themselves and reach the same insights you uncovered. These visuals build trust in your findings and recommendations.

When you convey your findings, you can shape your presentation/ report/narrative in a way that mirrors the movie arc:

• How you noticed the issue (“Three weeks ago, we were…”)
• The anomaly you saw (“We noticed something strange…”)
• What you initially thought (“At first, we believed…”)
• What you analyzed (“Hence we explored…”)
• How your understanding changed (“But then we discovered…”)
• The insight you arrived at (“Eventually, we found…”)
• What can be done (“So now, we recommend…”)
• The benefits (“…so that we can…”)
• And the path forward (“The first step is to…”)

Depending on the depth of the problem, your entire narrative might hinge on a single data chart or unfold across a sequence of them. In this way, the data supports the narrative—not the other way around.

Insights

If this structure makes sense, two insights naturally follow.

Insight #1: Data storytelling is not a single skill. It’s a “competency” of four skills.

• Narrative skills – to frame the story and guide the audience
• Data Analytical skills – to identify the root cause
• Data Visualization skills – to translate the data into self-explanatory charts/graphs
• Business acumen – to make recommendations that actually work

By taking a competency-based approach, we can focus on the skills, knowledge, behaviors, and attitudes required to perform the task effectively.

Insight #2: Data storytelling leads with the narrative—not the data.

There are two reasons for this.

First, the narrative structure stays relatively stable. It flexes a little depending on your audience (client, leadership, internal team) and the purpose of the discussion (decision-making, alignment, budget approval, etc.), but the overall arc remains the same.

Second: Because you can see your whole story from start to finish, it’s easier to choose the right data and visuals to support it. You don’t need to show everything—just what helps the story make sense. This doesn’t mean you’re cherry-picking data to mislead—just showing enough to help the audience see what you saw.

Based on these 2 insights, here’s the updated model for data storytelling:

A Word of Caution

Sometimes, even the best data stories don’t always land.

In many organizations, data quality is still a known issue. Leaders are aware of it, which makes them hesitant to act purely on data-driven insights. Rightly or wrongly, they see risk in trusting numbers they don’t fully believe.

Then there’s the human factor. Intuition continues to play a big role in decision-making—especially at senior levels. Leaders often lean on experience and gut instinct, which means they may accept data when it confirms their view and dismiss it when it doesn’t. That’s confirmation bias in action.

These cultural challenges influence how your story is received. The good news is that while you can’t control the culture, you can control the clarity, structure, and credibility of your narrative through your use of data. We are here to help.