Lauren Argenta
Design director at Thinkmill. Loves wireframing, all things user research, and solving complex problems using design and engineering collaboration.
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Core concepts
Core concepts are an expression of a project‘s underlying data model and relationships, translated into a written format that the team can build an aligned understanding on.
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Design Ethnography
Adopted from the world of Anthropology, Design Ethnography is a research method that captures everyday life to discover interesting and unseen user experience opportunities.
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Functional wireframing
Functional wireframing documents how the various pages and interface elements of an app or website need to come together in order to support the user.
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Low-fidelity prototypes
Low-fidelity prototypes bring interactivity to functional wireframes to in order to test and better understand how a product or app needs to respond to user interaction.
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Process mapping
Process mapping surfaces the movement of information and user interaction throughout a system in the form of a flowchart. It captures assumptions or current-state realities related to how the system works, and helps expedite alignment, design, and development,
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Playback
Playbacks are checkpoint updates to a wider audience than the core team. They share progress to garner feedback and ensure delivery aligns with expectations before proceeding further.
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User journey mapping
User journey mapping captures the steps a user takes to complete a specific task in the form of a flowchart. It surfaces assumptions about how the team expects a user to interact with the product, and highlights pain points and areas for improvement.
talk
How to establish multi-disciplinary teams to effectively design and build products
Lauren recently co-presented with Paul Merrell from Meld Studios at UX Australia 2022 about how Thinkmill & Meld established a multi-disciplinary team to effectively design and build a product.
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Engineering-led design
Lauren Argenta describes Thinkmill’s Engineering-led Design mindset, and the 5 behaviours that a cross-disciplinary team can adopt to make work life (and the software we make) better.
More posts from other Thinkmill voices
Is Your Design System Ready for AI?
AI can generate convincing interfaces, but it struggles when a design system’s decisions live in team knowledge rather than its components, documentation and defaults. Preparing a design system for AI means making those decisions discoverable, explicit and verifiable.
·6 min readBuild vs buy: AI code review for teams that have opinions
There’s a good chance that somewhere in your org, someone is trialling an AI code review tool right now, and at the same time someone else is pasting agent output into PRs under their own name.
·6 min readOptimise Your Context, Not Your Prompts
AI makes better engineering decisions when it can understand a project’s architecture, conventions and shared knowledge. Making that context clear and discoverable helps it produce software that fits the wider system.
·5 min read
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