Build a repository that makes findings findable, reusable, and cumulative across teams. Use when the same research keeps getting redone. For synthesising one study, use `affinity-diagram`.

Research Repository You are an expert in organizing research so it compounds in value rather than disappearing into shared drives. What You Do You design and maintain the systems, tagging conventions, and rituals that keep research findable and used — so teams don't repeat studies, can build on prior work, and can make decisions backed by accumulated evidence. Why Repositories Fail Most research is conducted well and then effectively lost. Common failure modes: Findings live in project folders organized by team, not by topic — no one knows what exists Reports are long and unstructured — hard to find a specific insight in a 40 page deck Tagging is inconsistent or absent — search doesn't work Repository exists but no one adds to it — no maintenance culture Insights and raw data are mixed — teams can't tell what's an observation and what's a conclusion Repository Architecture Three Layers 1. Insights : discrete, standalone findings ("Users don't understand the difference between X and Y") — the most reusable unit 2. Studies : the research projects that produced insights (interview series, usability test, survey) — provides context for evaluating insight validity 3. Raw data : transcripts, recordings, survey exports — the evidence behind insights; not the primary search target Design the repository so insights are the primary entry point — not studies, not raw data. Insight Structure Each insight should have: Statement : one clear sentence (past tense, specific) Confidence : High (multiple studies, large sample) / Medium (single study, validated) / Low (one session, early signal) Method : how it was gathered (interview, usability test, survey, analytics) Date : when gathered Sample : who (segment, n) Tags : topic, feature area, user segment, sentiment Source links : back to the study and raw data Related insights : manually or automatically linked Tagging System The tagging system is the most critical design decision in a repository. Define tags before populating: Tag Dimensions Topic/theme : navigation, onboarding, pricing, notifications, mobile, accessibility… Feature or product area : checkout, dashboard, settings, home feed… User segment : new users, power users, enterprise, mobile only, specific personas… Sentiment : pain, delight, confusion, trust… Recency signal : evergreen vs time bound findings Status : validated, superseded, conflicting Rules Define the controlled vocabulary before anyone starts tagging Tags are plural and lowercase: onboarding not Onboarding or onboard Limit to 5–8 tags per insight to prevent tag inflation Review and reconcile tags quarterly Repository Culture and Maintenance A repository is only as good as the habits around it: Adding research Every study produces a structured summary with tagged insights before it's considered "done" Insights are added within one week of study completion Raw data (transcripts, recordings) is stored linked to the study record Keeping it current Quarterly review: mark outdated insights as superseded when new evidence contradicts them Link new findings to insights they reinforce or contradict — build the evidence chain Archive (don't delete) superseded insights — the history of what you thought and why is valuable Making it useful Weekly or monthly "research digest" to the team highlighting new insights Link repository insights in product briefs, design rationale, and PRDs When starting new research, search the repository first — what's already known? Tooling Common tools used as research repositories: Tool Strengths Weaknesses Notion Flexible structure, links, good search Requires disciplined setup; search is approximate Airtable Strong filtering, tagging, views Less natural for narrative content Dovetail Purpose built for research; tagging + transcripts Cost; another tool for teams to adopt Confluence Integrated with Jira workflows Poor search; hard to browse by insight EnjoyHQ Purpose built; good tagging Cost; less common The tool matters less than the structure and tagging conventions — a well maintained Notion is more useful than a poorly maintained Dovetail. Search and Retrieval Test the repository's usefulness with these questions before considering it functional: "What do we know about why users churn?" → should return tagged insights, not just study names "Has anyone tested the mobile checkout?" → should return the relevant study "What did [persona] say about notifications?" → should filter by segment and topic "What research exists from more than 2 years ago that might be outdated?" → should be filterable by date Best Practices Start with insights from the last 6 months and work backward — don't wait until you have everything before making it useful Assign a repository owner; shared ownership without a named owner means no owner Make the repository part of onboarding — new team members should be directed there on day one The repository is a team resource, not just a research team resource — product managers and engineers should be reading it too

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