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Latent Scope: Finding structure in unstructured data
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Wednesday, June 11, 2025 • Designing with AI 2025
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Latent Scope: Finding structure in unstructured data
Speakers: Ian Johnson
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Summary

As a data visualization designer and developer the challenge I often face is what to do with unstructured data. One case study I can show is exploring survey results where the multiple-choice questions are straightforward to analyze but interesting open-ended questions like “What do your colleagues not understand about data visualization?” are much harder to crack. Latent Scope is an open-source tool I built that streamlines a process of embedding text, mapping it to 2D, clustering the data points on the map and summarizing those clusters with an LLM. Once the process is done on a dataset structure emerges from the unstructured text, allowing us to get a sense of patterns in the survey answers. Themes like “the time it takes” to develop data visualization pop out, as do “the importance of good design.” While people don’t use the same language to describe these themes, they show up as clusters in the tool thanks to the power of embedding models. https://github.com/enjalot/latent-scope

Key Insights

  • Unstructured data poses significant analytical challenges, especially when dealing with qualitative responses in large datasets.

  • AI techniques like embeddings and dimensionality reduction can effectively map the structure of unstructured data, revealing hidden patterns.

  • Juxtaposing user data through dimensional reduction can yield unexpected insights about product usage and user behavior.

  • Latent Scope simplifies the process of analyzing unstructured data, making advanced techniques accessible to non-experts.

  • Finding similar patterns in diverse data, such as sketches or survey responses, can uncover insights that traditional analysis methods miss.

Notable Quotes

"I want to take you on a bit of a journey of how I came to care about unstructured data."

"Organizing these responses is often a manual labor of love."

"This set of techniques and embedding, mapping, clustering and then visualizing data sets is not new, but daunting to execute."

"What if you didn't know there were important questions you should be asking?"

"I wanted a way to turn it into a robust and easy to use process."

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