What is data visualization? Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

Sébastien Pierre is the founder of FFunction, a Montréal-based data visualization company. His company created an insightful infographic titled: "What is Data Visualization?" We asked Pierre for an in-depth explanation about the infographic and how it defines data visualization in the modern era of data journalism and information architecture.

This comprehensive data visualization infographic effectively illustrates the complex intersection of multiple disciplines needed to transform raw data into visually digestible knowledge. It outlines the essential fields, raw elements, and core competencies required to succeed in visual design.

The Core Components of Data Visualization

According to Pierre, definitions for the terms in the infographic are mapped systematically as follows:

• Fields: Design, Communication, Information and their mix: Visual Communication, Data journalism, User Interface
• Raw elements: Look & Feel, Idea, Data
• Disciplines: Journalism, Information Architecture, Typography
• Process elements: Visual Design, Objective, Dataset
• Outputs: Layout, Story, Report, Data Analysis, Dashboard, Interface
• Final result: Form, Concept, Knowledge
• Core competencies: Readability, Logic, Usability
• Core values: Simplicity, Informativeness, Relevance

The Data Discovery Process

Data visualization starts with being a journalist to some extent, Pierre explained. You need to find the raw data and analyze it rigorously. This discovery process means finding datasets that are of high quality, verifiable, and completely up to date. Without reliable data, even the most beautiful layout fails its primary informative objective.

It's also fundamentally about information and communication, Pierre added. Visualization should reveal hidden patterns and trends within the data that would otherwise remain obscured in a spreadsheet. It should explore a topic deeply, help make a discovery, or tell a compelling story. Whatever the ultimate goal, you have to transform the raw dataset into meaningful information that people can easily understand and act upon.

Formalizing the Visual Design

Once the information and direction are clearly defined, the visualization itself needs to be carefully formalized. This step involves critical decisions regarding user interface and visual design.

"Will it be interactive or static? Will it be used as a tool or to illustrate something? Depending on how we position the visualization, it will be more demanding on UI aspects or on visual aspects. Dashboards, online reports and interactive web visualizations need a solid understanding of UI design, while infographics and print reports require a strong foundation of typography, layout and visual communication."

The design ties it all together seamlessly. It makes the visualization visually appealing and intellectually interesting. The design must strike a perfect balance between the simple and the complex. The data visualization needs to communicate the inherent complexity of the information without losing its richness, depth, or context.

The Future of Information Architecture

Data visualization requires profound expertise from multiple disciplines. It seamlessly mixes different perspectives. It is exactly what Pierre and his group successfully built into this remarkable infographic. The art of developing infographics with big data draws heavily from the established world of print journalism, statistical analysis, and the graphic arts.

It will continue to rely on these foundational disciplines. The major difference moving forward will be universal accessibility. More people will have access to powerful tools and the knowledge needed to create infographics that simply communicate complex concepts while also maintaining a richness that helps express a deeper meaning. As information architecture evolves, so too will our capacity to understand vast datasets through visual communication.

Frequently Asked Questions (FAQ)

What is data visualization in simple terms?

Data visualization is the process of translating large amounts of data and information into a visual context, such as a map or graph, to make data easier for the human brain to understand and pull insights from.

Why is data visualization important?

It is important because it allows users to quickly spot trends, patterns, and anomalies in large datasets, facilitating faster and better data-driven decision-making processes across various industries.

What are the key elements of a good visualization?

A good visualization balances form and function. It requires high-quality data, a clear objective, excellent readability, usability, and an appropriate visual design that accurately reflects the underlying information without distortion.