170  |  Formalizing Design with Gabrielle Mérite and Alan Wilson

2023-08-03 · Source: Data Stories · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, extended

Summary

This Data Stories podcast episode, hosted by Moritz Stefaner with guests Gabrielle Mérite and Alan Wilson, explores the emerging field of data design systems, style guides, and design languages. The discussion covers various projects, including Adobe Spectrum, Deloitte's Insights Magazine, and the WHO Data Design Language, highlighting the purpose and value these systems bring to organizations. Key benefits identified include increased efficiency, consistency, scalability, and brand recognizability in data visualization outputs. The conversation also delves into the challenges of defining scope, audience, and the dynamic nature of these systems, emphasizing the need for flexibility and continuous evolution. The guests share insights on their design processes, from initial analysis and creative direction to the integration of design tokens and the importance of user-centric tooling.

Key takeaway

For design leaders and data visualization professionals developing internal standards, recognize that design systems are dynamic products requiring continuous internal maintenance and user feedback. Prioritize delivering incremental value and adapt tooling to meet users where they are, rather than imposing rigid, static guidelines. Emphasize underlying principles like accessibility and ethical considerations, allowing for flexibility in detailed implementation to foster broader adoption and long-term utility.

Key insights

Data design systems enhance efficiency, consistency, and brand recognition in organizational data visualization.

Principles

• Design systems must be living, evolving entities.
• Define purpose and audience before building guidelines.
• Balance standardization with creative impact.

Method

Start with an analysis phase to understand organizational needs and existing patterns. Develop design first, testing basic charts to find patterns, then systemize into guidelines and templates, incorporating creative direction and ethical considerations.

In practice

• Provide resources in users' preferred tools (e.g., Excel, PowerPoint).
• Utilize design tokens for flexible, scalable design changes.
• Split guidelines for designers and non-designers.

Topics

• Data Design Systems
• Data Visualization Guidelines
• Adobe Spectrum
• Ethical Data Design
• AI in Design

Best for: Product Designer, Software Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Stories.