Suggestions
Dan Brown
Information Architect and Principal at Curious Squid, LLC, an information architecture + UX consultancy (ex-EightShapes)
Dan Brown is a prominent figure in the web design and information architecture field, known for co-founding EightShapes, a user experience design studio based in Washington, DC with Nathan Curtis.
With a Bachelor's degree in Philosophy from Wesleyan University, Dan has an extensive background in design discovery, information architecture, interface design, user research, and usability testing.
Having held various roles including Information Architect, Principal, Author, and Founder in organizations like Curious Squid, School of Visual Arts, and New Riders, Dan has a wealth of experience in managing projects, client interaction, and conflict resolution.
Dan is a seasoned workshop instructor, sharing insights on discovery, facilitation, and collaboration at events like Confab and Enterprise Experience.
His diverse career includes positions such as User Experience Lead at Computech, Inc, Content Management Lead at Transportation Security Administration, and Director of Information Architecture/Creative Director at USWeb/marchFIRST.
Dan Brown's expertise lies in leading teams, project planning, and fostering collaboration to deliver successful large web design projects with a focus on complex interactivity, big content, and responsive design.
Highlights
Doing information architecture for digital products is not that much different than doing information architecture for content.
That said, doing IA from a product design perspective offers new ways to look at the practice.
In this article I talk about the 5 challenges I see as essential to doing information architecture for products.
Figma announced several new features using Generative AI. Far more interesting than the features were the responses from designers. Although many were worried about the increasing role of AI in design, other responses lamented the narrowing understanding of design. To say that AI is coming for our jobs is to reduce design to putting pixels on a screen. We expect team members to understand that we’re not just rectangle painters, and yet sometimes we forget it ourselves.
My holistic view of design emerged when I researched creativity and innovation, seeking out the behaviors and mindsets that enable the best creative work. I summarized what I learned in three mindsets -- curiosity, skepticism, and humility -- that are essential for individuals participating in creative endeavors. Moreover, I sought a model of design that didn’t prescribe a particular series of tasks, that didn’t focus exclusively on the deliverables, and that could scale to accommodate different sized problems and different sized constraints.
I developed a model called the Discovery Activities Matrix. This two-by-two doesn’t prescribe particular activities or deliverables. Instead it helps designers and teams be intentional about where they are in developing their ideas and adjusting their behaviors to meet the needs of the object of their work. The model addresses four types of activities:
~ Gather: Filling gaps in your knowledge and understanding to inform the process ~ Process: Analyzing new information to put it into context for the problem ~ Explore: Considering a variety of approaches for addressing the problem ~ Focus: Zeroing-in on a single direction
How does creativity work? My hypothesis is that we’re drawn to one of these four types of activities depending on the need of the thing we’re working on. Maybe we have a hunch that the enterprise needs a tool to support a business process, so we GATHER to see how that process works today. Maybe we’ve confirmed our hunch, but we don’t know enough to know what’s important, so we move to EXPLORE to consider a variety of solutions. Using those explorations, we move to FOCUS to identify the right constraints, even if we haven’t found the solution yet. At any given moment, I’m doing one of these types of things in service of the design.
This movement between the activities is guided by our perception of what the work needs at any given moment. It’s a compelling philosophy, but a bit naive. The real world doesn’t give us the luxury of floating along until we find a solution. That doesn’t diminish the strength of the model. I know what I need to do, I know what’s missing from my process, even if I’m constrained from doing it.
Frankly, there’s a role for AI in every one of these quadrants, but understanding what the work needs at a given moment is a decision for a human to make. Not because an AI can’t do it (eventually), but because listening to the work and deciding where to take it is the ultimate act of creativity.
