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The Problem Space
Summary
Over 3 decades of software design and development, we have seen the creation and proliferation of tools that help our teams be great at the solution space - diagramming, sketching, prototyping, tracking, measuring. How might we make ourselves better at working in the problem space? In this videoconference, we brainstorm and explore the topic, and maybe even inspire someone to build that perfect tool.
Key Insights
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There is a wide variety of prototyping tools, but tooling for early problem space research is underdeveloped.
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UX researchers spend significant time manual coding and synthesizing interview transcripts without much tool automation.
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Language mapping using natural language processing could help extract user narratives and keywords to better understand user thinking.
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Data repositories across organizations often fail due to tool complexity and lack of cross-disciplinary accessibility.
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Collaborative tools like Mural facilitate real-time brainstorming and affinity mapping in remote teams.
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Hypothesis definition is a largely social and iterative process, reliant on team communication rather than technical tooling.
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Mapping assumptions from user and business outcomes supports prioritizing testable prototypes early.
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Tooling that connects research questions, assumptions, tests, and outcomes could provide critical ‘glue’ for the research process.
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Research approaches differ greatly depending on context, from startups to established enterprises requiring flexible tooling.
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Remote work increases reliance on digital collaborative tools but also surfaces challenges in managing distributed inputs and synthesis.
Notable Quotes
"We have eight million prototyping tools but not many tools that help us spend more time in the problem space."
"I suspect there are specific tasks we could support more effectively with dedicated tools."
"Langauge is what shapes us, and extracting keywords helps us create narratives to understand how people think."
"Data repositories often fail because of the complexity in cataloging and who can access the information."
"We still do a lot of coding transcripts by hand, and I’m not sure where the ideal digital intersection is."
"The hardest thing with hypotheses is looking for the disconfirming evidence, not just the confirmatory."
"I’d love a tool that combines research questions, assumptions, tests, and outcomes all in one place."
"Different organizations need different approaches; it’s hard to generalize research tooling needs."
"Tools like Mural are great for remote real-time sticky note brainstorming and affinity mapping."
"Once you have a testable prototype, you can validate and iterate quickly, which makes a broad early approach useful."
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