How would you architect a centralized research repository for heterogeneous data types?

WHAT THIS TESTS: This question evaluates your ability to design a socio-technical system that transforms raw heterogeneous UX artifacts into a structured discoverable knowledge base. The interviewer cares about your understanding of data pipelines, information architecture, and search infrastructure in a research operations context, not just your familiarity with UX methods or report writing.

A GOOD ANSWER COVERS: A strong response walks through four layers in order. First, ingestion: describe automated transcription for video, parsing and normalization for survey responses, and entity extraction so every artifact becomes machine-readable. Second, storage: propose a unified metadata schema where insights, transcripts, clips, and raw notes link back to studies and participants via persistent identifiers. Third, indexing: explain a search index that supports full-text search across transcripts and tags, plus faceted navigation by study date, method, participant segment, or theme. Fourth, cross-referencing: show how relationships are explicitly modeled, such as linking a survey quote to a video timestamp and the insight it supports, so users can trace evidence from claim to primary source.

COMMON WRONG ANSWERS: Red flags include proposing a simple folder hierarchy in a generic drive like SharePoint with no structured metadata, which replicates the silo problem. Another mistake is optimizing only for video search while treating surveys as static PDFs, missing the need for unified querying across qualitative and quantitative data. Suggesting manual tagging without automation also signals a design that will not scale past a handful of studies.

LIKELY FOLLOW-UPS: Interviewers often push on versioning when insights evolve, access control for sensitive participant data, or how to prevent the repository from becoming an uncurated data swamp. They may also ask how you would measure adoption, trust, or return on investment for the ResearchOps team.

ONE CONCRETE EXAMPLE: Imagine a repository receiving a usability test. The ingestion pipeline transcribes the session video and extracts timestamped utterances. A researcher tags a segment as checkout friction. The system links that tag to a related survey response where twelve participants rated checkout ease as low. When a product manager later searches checkout friction, they see the insight, the video clip, and the quantitative survey trend, all cross-referenced under a shared theme taxonomy that updates across studies.

Source: nngroup.com