Henny Speelman: The A.R.T. of Data Interpretation

Blending Artistic Insight with Analytical Thinking to Tell Data-Driven Stories!

Every person has that one childhood dream they wordlessly tuck away when life steps in. For Henny Speelman, that dream was art. He loved the silence of creation, the smell of paint, the sync of brushstrokes, the feeling of creating something from nothing. He imagined a life filled with color and emotion, but the world around him encouraged something more practical. So, he set his dream aside, believing he had moved on.

Except, he never really did. The urge to create stayed, quiet but steady, waiting for a place to belong. That place appeared years later, in the most unexpected form: data. At first, it seemed distant from everything he loved. But as Henny looked closer, he saw something familiar. Patterns moved like brushstrokes. Shapes carried meaning. Color guided emotion. It was art, just speaking a different language.

That discovery became the foundation of his A.R.T. model: Attention, Recognize, Takeaway. It portrays how people connect with visuals, what captures them, what feels meaningful, and what they carry with them after. Through this, he turned raw information into something people could see, feel, and remember.

For him, data visualization is about telling a story that lives within those facts. A story that helps people feel the movement within the numbers. Henny’s journey proves that creativity never truly fades. Sometimes, it just finds a new canvas. And for him, that canvas turned out to be made of data.

Let us learn more about his journey:

Where the A.R.T. Model Truly Begins

The A.R.T. model, for Henny, is much more than a framework for visuals, it is an introspection of how people experience the world. Henny believes the A.R.T. model can be applied to almost every aspect of life, whether someone is looking at a painting or crossing the street. That is exactly what makes the A.R.T. model so relatable for companies. It connects how people naturally see the world with how they interpret data.

For many, data feels abstract or dry, but when combined with the principles of A.R.T., it suddenly becomes human again. He explains that it may begin with mindset, but once the model is understood, it becomes automatic.

The Role of Leadership and Team Communication in Data Trust

Trust is crucial within an organization. When it comes to data, Henny sees trust as having three distinct pillars: the person, the visual, and the data itself. Each of these pillars carries a different level of impact when something goes wrong.

If the data itself is flawed, it takes a lot of effort to rebuild trust. If a visual is confusing, recovery is possible, but still challenging. If the communicator makes a small mistake, that is usually easier to forgive.

He believes leadership sets the tone by creating a culture where trust is essential. However, teams must know how to build that trust every day through communication, clear visuals, and reliable data.

A Moment When a Company’s Mindset Toward Data Shifted

Henny has seen company mindsets shift the moment they realize how much communication shapes the way people perceive data. It often happens when he is presenting with the A.R.T. model in mind. At first, people tend to question the visualization, asking things like, “Why did you use this chart?”

That reaction shows how fragile trust can be when visuals or messages fail to align perfectly. But when they see a visualization that supports the story instead of competing with it, they stop questioning the chart and start engaging with the insight. When the communication is clear, people no longer doubt the data.

Shifts Businesses Must Make in Storytelling with Data in the Age of AI

There is a shift from a task-driven paradigm to a results-driven one. In the past, people needed technical knowledge to build a chart. Today, it has become an era where one can simply describe what is required, and an AI model will generate it. This change opens the door for more people to create visualizations even without technical expertise.

However, it also means companies must focus on training people to understand the fundamentals, such as how to interpret data, choose the right chart, know the audience, and apply design principles. AI accelerates creation, but judgment remains human. It is still essential to know what a visualization should communicate and why it matters.

Turning Data Mistakes into Moments of Clarity

It often goes wrong when organizations try to tell stories with data without using a clear framework. That is why Henny created the M.U.S.E. framework, which stands for Main Subject, Unveil, Story, and Exit. One of the biggest challenges today is that the world is saturated with information. Everyone tries to tell everything at once. But good storytelling requires focus. It is essential to know the main subject and how to explain the chart.

He often observes presenters assuming that everyone in the room instantly understands their charts. In truth, data visualizations need to be decoded. While the presenter is explaining the insight, the audience is often still trying to make sense of the visual. That disconnect should never occur, and his framework helps prevent it.

Storytelling as a Bridge Between Data and Team Unity

According to Henny, data storytelling rests on three pillars: data, visual design, and narrative. The narrative is where tone and emotion come into play, and emotion is often the missing link in how leaders communicate.

In Belgium, emotion in business is still seen as something to avoid. However, he believes that storytelling helps bridge that gap, as it allows leaders to use data to feel and to connect.

The Foundation for Shared Trust in Data

A key aspect of trust, Henny explains, is governance. That means both data governance and tool governance. It is about aligning people, processes, and technology so everyone knows exactly what they are looking at. Strong governance is the foundation of reliable decision-making.

Because at the end of the day, trust in data comes down to communication. If users discover data issues on their own, trust is lost.

First Steps Toward Clarity and Confidence in a Tangle of Reports and Dashboards

While the A.R.T. model focuses on how people read and perceive charts, and M.U.S.E. focuses on how to tell stories with data, Henny uses the N.O.V.A. framework when organizations are tangled in too many reports and dashboards. N.O.V.A. stands for Need, Organize, Visualize, and Advance. It is a way to bring structure back into the reporting process.

It begins with identifying the needs of the audience and understanding which questions truly matter.

Then comes the Organize phase, where the reporting process is streamlined. The Visualize phase focuses on designing the right visuals to communicate insights clearly. Finally, the Advance phase ensures sustainability, the aftercare that most companies often overlook.

Unique Patterns & Challenges in Belgian Organizations’ Approach to Data Communication

Henny observed that many Belgian organizations collect more than they connect. They gather every possible dataset they can access, but they rarely take the next step of combining those insights to create real context.

For example, they might have solid internal sales data but never link it with external factors like local events that could explain or even predict trends. The technical capability is often there, but the mindset to connect those data points is missing.

The Role of Storytelling in Today’s Data Landscape

Henny’s message to every audience is to start looking at data differently. Too often, data is seen as something technical, geeky, or detached from real decision-making, while many still rely mainly on gut feeling. He believes that a good data visualization can either support intuition or challenge it.

When designed well, a visualization activates the brain in ways that make insight click. His mission is to show that data, when communicated through storytelling and design, can help people make decisions they truly trust.

The Evolution of Making Data Communication More Human, Clear, and Trusted

Henny’s mission began with a technical focus, such as choosing the right chart or colors. Over the years, he has seen that data communication is about people. His mission has evolved into something more human.

In the current age of AI, he observes that many organizations still struggle with the basics, having clean, reliable data and a culture that values critical thinking.

That is why his focus for the future is to help people stay human in how they work with data, to pause, to question, and to truly understand before acting. AI is here to stay, and he embraces it, but never at the expense of the human element that gives data meaning.

Quotes:

“From my point of view, trust is crucial within an organization. When it comes to data, I see trust as having three distinct pillars: the person, the visual, and the data itself. Each of these pillars has a different level of impact when something goes wrong.”

“If the data itself is flawed, it takes a lot of effort to rebuild trust. If a visual is confusing, recovery is possible, but still challenging. If the communicator makes a small mistake, that’s usually easier to forgive.”

“Leadership sets the tone by creating a culture where trust is non-negotiable. But teams must know how to build that trust every day, through communication, clear visuals, and reliable data.”