Designing for DEEP meant working within complex legacy systems, addressing highly specific organizational workflows, and simplifying dense, qualitative data structures. The tool had to meet the needs of diverse actors while making vast volumes of unstructured information clear, searchable, and usable without compromising the flexibility expert users rely on.

Extensive user research was central to the project’s success. The YU team conducted numerous interviews as well as extensive surveys with users across individual organizations performing secondary data analysis, as well as with global taggers responsible for manually structuring vast datasets. These insights guided the design to balance clarity and flexibility, ensuring the platform meets diverse workflows and user needs worldwide.

The project significantly improved the efficiency and accuracy of secondary data tagging, reducing manual workload and streamlining analysis. A well-organized components library ensures consistent, scalable design across the platform. By integrating natural language processing (NLP) and large language models (LLMs), the system can automatically analyze and tag content, enhancing data insights and empowering users to make faster, more informed decisions.