Session
🇳🇱 The 'Gap' in Dashboard Design, why the theory of Stephen Few is still actual.
An effective dashboard starts with strong design and deliberate choices. Too often, poor visual decisions influence the final outcome and blur the message we intend to communicate. As a result, a gap can emerge between the original objective and how the audience actually perceives it.
In this inspiring session, we begin by exploring the psychology behind data visualization: how our brain processes information, the role of the designer as an information director, and how to ensure your message truly reaches the intended audience.
A key part of the session highlights Stephen Few’s timeless framework: “The 13 Most Common Mistakes.” Even years after publication, these principles remain remarkably relevant. By recognizing these mistakes—and understanding their impact—you can create smarter and more powerful visual solutions that truly resonate.
The session is richly supported with clear, relatable real‑world examples, with room for interaction and discussion. You’ll gain a solid understanding of the complete design thought process, along with insights into how Artificial Intelligence can support us in creating better visualizations and smarter decisions.
This session is a must‑attend for anyone willing to think beyond the obvious, challenge assumptions, and create better visual products. It is valuable for both beginners and experienced professionals.
This session explores the psychology behind data visualization and how design decisions shape the effectiveness of dashboards. Using Stephen Few’s classic “13 Most Common Mistakes,” we highlight how to avoid common pitfalls and create visuals that truly resonate. Supported by real‑world examples and audience interaction, the session provides practical insights into better design choices and how AI can enhance visualization quality. Relevant for both beginners and experienced professionals.
Wilfred Kluin
Fellowmind, Teamlead Data & Analytics & Data Coach
Deventer, The Netherlands
Links
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