This article explores the concept of data visualization, its evolution over time, its main applications across different fields, and ideas about its future development.

What is Data Visualization?

Data visualization is the process of turning data into a visual form. It helps people discover patterns, compare values, and quickly understand complex information.

Our perception of the information

Our brains don’t process information the way we assume. They’re not built to handle large quantities or abstract scales with precision. We estimate, we approximate, and often, we misjudge.

Consider this example. At one end, there is 1,000. At the other, 1 billion. Try to guess where 1 million would fit between these two numbers:

You could imagine 1 million positioned around the middle or slightly to the left, seeing it as much larger than 1,000 but still far from a billion.

At this scale, we cannot see the difference. One million sits so close to one thousand that the gap is almost invisible.

A picture's worth a thousand words

Long before spreadsheets or databases existed, we used drawings, maps, and symbols to make sense of the world. Our brains process images much faster than text, which is why a simple graph can sometimes explain more than pages of numbers ever could.

Since the dawn of time, humans have relied on visuals to make sense of their world. Ancient civilizations used maps to represent territory, star charts to track the sky, and symbols to record trade or agriculture. But the foundations of modern data visualization took shape much later.

A Quick Look Back

In the late 1700s, William Playfair introduced bar charts and line graphs to represent economic data. In the 19th century, Florence Nightingale used diagrams to argue for better hygiene in hospitals. Charles Minard’s famous map of Napoleon’s Russian campaign combined geography, time, and human loss in a way that still inspires data designers today.

In 1854, John Snow (not the one from Game of Thrones) mapped cholera deaths in London to reveal a contaminated water source. It’s one of the earliest examples of data visualization being used to drive a public health response :

Each black bar represents a cholera death at that address. Most are clustered around the Broad Street water pump, pointing to it as the source of the outbreak.

If you are interested in the history of data visualization, you can read more about it in the Yellowfin article on the subject.

Nowadays, data visualization is used in many fields: science, journalism, business, government, education, healthcare, finance, sports, and more.

It works across languages and cultures because it relies on visual perception. A well-designed chart can be understood by anyone without a technical background. That makes it a powerful tool for communication and decision-making.

The Growing Importance of Interpreting Data

Every day, huge amounts of data are generated everywhere.
Sensors in cities measure air quality and traffic levels. Devices track people’s health continuously. Digital platforms record user interactions by the second. Industries and laboratories produce streams of operational data without interruption. Without clear visualization, this overwhelming flow of information can be difficult to interpret.

Fortunately, tools for visualizing data have become much easier to use. They are no longer exclusive to experts or developers. People working in marketing, human resources, education, and even the arts now rely on visualizations to explore and communicate data.

This shift matters. When more people can build their own visualizations, they gain the means to question, explore, and explain data on their own terms. Access to tools changes the nature of interpretation: it moves from a specialized task to a broader cultural practice. In that sense, data visualization contributes to a more open and shared understanding of the world we live in.

Use Cases and Applications

Data visualization is applied across a wide range of fields and serves multiple purposes :

Science & Research

Visualization supports the analysis of large datasets. It helps detect trends, validate hypotheses, and interpret complex results in fields such as medicine, environment, and social sciences. Researchers use heatmaps to study gene expression, line charts to track climate variables over time, or network graphs to analyze social interactions. Visualization also plays a key role in simulation and modeling, allowing scientists to test scenarios and understand complex systems, such as epidemic spread models or ecological dynamics.

Journalism & Communication

Visualization presents data to explain facts and highlight social, political, or environmental topics. Maps and charts clarify information for a broad audience. Journalists use infographics to summarize election results or economic indicators, while NGOs create visual reports to illustrate the impact of humanitarian crises. These visuals help communicate complex information in an accessible way.

Education

Teachers use interactive graphs and visual tools to explain concepts in mathematics, science, history, geography, and more. Educational platforms incorporate data visualization to track student progress and adapt teaching methods accordingly.

Business

Data visualization helps monitor key performance indicators and track activities. Dashboards assist managers in identifying issues and making decisions based on data summaries. Sales teams use dashboards to follow revenue trends and adjust strategies quickly. In operations, real-time monitoring of equipment status enables timely maintenance and reduces downtime. Visualization tools also support communication within organizations by providing clear reports that align teams on objectives.

Engagement

Data visualization plays a key role in increasing user engagement within applications. By showing users their own data in an interactive and accessible way, it encourages exploration and personal connection with the information. Features such as filtering, zooming, and real-time updates allow users to interact actively with data. Gamification elements like progress bars, badges, or data-driven challenges keep users engaged.

Emotional Design

Beyond traditional charts, data visualization can serve as an artistic or experimental medium. Designers and artists create sensory or immersive experiences that evoke emotions and provoke reflection. Examples include interactive installations reacting to environmental data like air quality or noise, and abstract representations of personal data such as heart rate or sleep patterns. These approaches engage users on a deeper emotional level and invite contemplation of our relationship with data and technology.

These examples demonstrate that data visualization is much more than a graphical tool. It is a fundamental means of understanding, communication, and decision-making in the context of widespread data availability.

My Vision for the Future

Data visualization is steadily advancing, and I believe the future lies in making it more accessible and easier to create. Simplifying the process of designing interactive and creative visualizations will allow more people to explore and understand what data truly reveals.

That is why I am currently developing Oblisk, a tool that makes creative, interactive, and immersive data visualization accessible to everyone (coming soon, stay tuned !)

By lowering barriers to entry, I hope to foster a wider culture of data literacy where interpreting and communicating data becomes a natural part of interacting with information in diverse contexts.

References

Tools you can use to create data viz

• Low code / no code :

  • PowerBI - A business analytics tool by Microsoft for building interactive dashboards and visualizing data from a wide range of sources.
  • Tableau - A leading data visualization platform known for its drag-and-drop interface and powerful analytics capabilities.
  • Flourish - A no-code tool for creating polished, interactive visualizations, often used by journalists and content creators.

• With code :

  • Observable Notebooks - Data notebooks with realtime collaborative editing.
  • D3.js - A low-level JavaScript library for building powerful and flexible data visualizations.
  • Chart.js - A simple, easy-to-use JavaScript charting library for creating responsive charts.
  • Apache ECharts - A powerful, extensible charting and visualization library developed by Apache, great for large datasets.
  • Plotly - A versatile graphing library for Python, JavaScript, R, and more; supports interactive, publication-quality graphs.
  • Matplotlib - A comprehensive Python library for creating static, animated, and interactive visualizations.

Books

• The Functional Art - Alberto Cairo’s comprehensive guide to information graphics, blending theory, design principles, and practical techniques to create effective visualizations.
• Beautiful Visualization - a collection of essays by leading data visualization practitioners that explores techniques and best practices for creating compelling visual stories from complex data.
• David McCandless books - Three visually rich books that explore how to present complex information simply and beautifully, with a strong focus on data visualization and storytelling.

Websites

• The Pudding - A digital publication that tells stories with data-driven essays and innovative visualizations.
• 1 dataset 100 visualizations - A project showcasing how a single dataset can be explored through 100 different visualization techniques.
• Dataviz Inspirations - A curated gallery featuring inspiring and creative data visualization examples from around the web.
• Mapzilla - Interactive maps and geospatial data visualizations.

I hope these resources help you deepen your understanding and inspire your own data visualization projects.

Thank you for reading. If you found this article useful, feel free to share it !