Research repositories vs. shared drives and wikis: choosing the right home for your insights

Every research team faces the same question eventually: where should all of this go?

After weeks of interviews, usability tests, survey responses, and synthesis sessions, the outputs need a home. Something searchable, something the rest of the organization can actually use. The choice usually comes down to three options: a shared drive like Google Drive or SharePoint, a wiki like Confluence or Notion, or a dedicated research repository.

Each approach has trade-offs. The right choice depends on how much research your team produces, how many people need to access it, and how long you need insights to remain useful. This article walks through the strengths and limitations of each option so you can make an informed decision.

What counts as "insight storage"?

Before comparing tools, it helps to clarify what research teams actually need to store. The outputs of qualitative research are not just files. They include:

Raw data — interview recordings, session videos, survey responses, diary entries, field notes
Processed data — transcripts, coded highlights, tagged quotes, annotated screenshots
Synthesized insights — findings, themes, recommendations, frameworks, journey maps
Context and metadata — participant details, study objectives, research questions, dates, methods used

Effective insight storage means keeping all of these layers connected. A finding is more credible when someone can trace it back to the specific quotes and sessions that support it. A recommendation is more persuasive when a product manager can watch the two-minute video clip that illustrates the problem.

Any storage solution needs to handle this range of content types while keeping them linked and discoverable. That is the standard against which shared drives, wikis, and repositories should be measured.

Shared drives: familiar but limited

Shared drives—Google Drive, Dropbox, OneDrive, SharePoint—are the default starting point for most teams. They are already available, everyone knows how to use them, and they require no additional budget approval.

What shared drives do well

Zero learning curve. Everyone on the team already knows how to create folders and upload files.
Broad file support. You can store documents, spreadsheets, videos, images, and PDFs in one place.
Access control. Most shared drives offer folder-level permissions, making it straightforward to restrict sensitive participant data.
Low cost. Many organizations already pay for cloud storage through existing productivity suites.

Where shared drives break down

Folder structures do not scale. When you have five studies, a folder hierarchy works fine. When you have fifty, it becomes a maze. Should the usability test from Q3 live under the project folder, the product area folder, or the method folder? Every organizational scheme is a trade-off, and no single folder structure accommodates every way someone might look for past research.

Search is limited to file names and document text. If a product manager wants to know what your team has learned about onboarding friction, they need to know which files to open. Shared drive search does not understand research concepts, tags, or themes—it just matches keywords in file names and document bodies.

No connections between artifacts. A video clip, a transcript highlight, and a synthesized finding might all relate to the same insight, but a shared drive has no way to link them. Each file exists in isolation.

Insights go stale silently. There is no mechanism to surface relevant past research when a new project begins. Old findings sit untouched in nested folders, effectively invisible.

No support for multimedia analysis. You can store a video file in a drive, but you cannot tag a moment within it, attach a quote to a timestamp, or clip a segment to share with stakeholders.

Shared drives are built for file storage. Research teams need insight storage. The gap between those two things grows wider with every study you complete.

Wikis: better for narrative, weaker for structure

Wikis like Confluence, Notion, and Slite represent a step up from shared drives. They allow teams to write up findings in rich-text pages, organize them with nested page trees or databases, and link between related topics. Many research teams adopt wikis as their primary insight home, especially when the broader organization already uses one for documentation.

What wikis do well

Rich text and embedded media. Wiki pages support formatted text, images, embedded videos, and tables, making it easy to create polished research reports.
Internal linking. You can link between pages, creating connections between related studies or themes.
Collaborative editing. Multiple people can contribute to and refine a page, which is useful for synthesis.
Organizational adoption. If your company already uses Confluence or Notion, publishing research there puts it where stakeholders already look for information.

Where wikis fall short

Wikis are page-oriented, not data-oriented. A wiki page is essentially a document. It works well for presenting a narrative summary of findings, but it is a poor container for granular data like tagged quotes, coded highlights, or participant-level observations. Research is not just a written report—it is a body of evidence, and wikis flatten that evidence into prose.

Findability decays quickly. Wiki pages are easy to create but difficult to maintain. As the page count grows, the information architecture becomes tangled. Pages get orphaned, naming conventions drift, and the search function returns too many results to be useful. Teams often discover that nobody reads past research in the wiki because nobody can find it.

No taxonomy or tagging system. Some wikis (Notion, for example) offer database properties that function like tags, but these are manual and fragile. There is no enforced taxonomy, no way to see all insights tagged with a particular theme across every study, and no mechanism to suggest related findings automatically.

Multimedia handling is basic. You can embed a video in a wiki page, but you cannot tag specific moments, create clips, or link a transcript passage to its corresponding video timestamp.

No distinction between insight types. Wikis treat all content the same. A process document, a meeting note, and a research finding are all just pages. There is no structural difference between an insight backed by thirty interviews and an opinion someone typed up after a single conversation.

Wikis work well as a presentation layer—a place to share polished summaries with stakeholders. But they are not well suited as the system of record for an active, growing body of research.

Research repositories: purpose-built for the job

A research repository is a tool designed specifically for storing, organizing, and retrieving qualitative research. It treats research as structured data rather than as files or pages.

Dedicated repositories—Dovetail is one example—are built around the specific workflows research teams use: importing transcripts and recordings, highlighting and tagging passages, clustering themes, synthesizing findings, and sharing evidence with stakeholders.

What repositories do well

Structured tagging and taxonomy. Repositories allow teams to build and maintain a consistent tagging system across all projects. When someone tags a quote with "onboarding friction," that tag connects to every other quote, clip, and finding with the same label, regardless of which study produced it.

Evidence traceability. Insights are linked to the raw data that supports them. A stakeholder reading a finding can click through to see the exact quotes, video clips, and session notes behind it. This makes findings more credible and easier to act on.

Cross-project search and discovery. Instead of searching by file name or page title, teams can search by theme, participant segment, product area, research question, or date range. This makes it far easier to answer questions like "What have we learned about enterprise onboarding in the last 18 months?"

Multimedia-native. Repositories handle video and audio as first-class content types. Teams can tag timestamps, create highlight reels, and share specific clips—all within the same tool where their transcripts and synthesis live.

Insight longevity. Because findings are tagged, linked, and searchable, they remain discoverable long after the original study is complete. This reduces duplicate research and helps teams build on what they already know.

Access for non-researchers. A well-organized repository gives product managers, designers, and executives a way to explore research findings without needing to ask the research team for a briefing. This scales the impact of research across the organization.

Where repositories require investment

Learning curve. A repository introduces new concepts—tagging taxonomies, highlight-level coding, insight objects—that require some ramp-up time for the team.

Taxonomy maintenance. A tagging system is only useful if it remains consistent. Someone on the team needs to own the taxonomy, merge duplicate tags, and ensure new projects follow existing conventions.

Cost. Dedicated repositories are a separate line item, unlike shared drives and wikis that may already be included in existing software subscriptions.

Adoption. Moving from a shared drive or wiki to a repository requires migration effort and buy-in from stakeholders who are accustomed to the old system.

These are real costs. But for teams conducting research regularly, the cost of not having a repository—in duplicated studies, lost findings, and insights that never reach decision-makers—tends to be much higher.

A practical comparison

Capability	Shared drive	Wiki	Research repository
File storage	✅ Strong	⚠️ Moderate (embedded)	✅ Strong
Rich text reports	❌ Limited	✅ Strong	✅ Strong
Structured tagging	❌ None	⚠️ Manual, fragile	✅ Built-in
Cross-project search	❌ Keyword only	⚠️ Keyword + page titles	✅ Tag, theme, and metadata search
Video/audio tagging	❌ None	❌ None	✅ Native
Evidence traceability	❌ None	⚠️ Manual links	✅ Automatic
Stakeholder access	⚠️ File-level	✅ Page-level	✅ Insight-level
Setup effort	✅ Minimal	⚠️ Moderate	⚠️ Moderate
Ongoing maintenance	⚠️ Folder sprawl	⚠️ Page sprawl	⚠️ Taxonomy upkeep

When each option makes sense

Not every team needs a dedicated repository on day one. Here is a rough guide:

Shared drives work when you are a solo researcher or a very small team, your organization conducts research infrequently (a handful of studies per year), and the primary consumers of research are the people who conducted it.

Wikis work when your organization already has a wiki that stakeholders actively use, you need a place to publish polished research summaries, and the volume of research is moderate enough that a disciplined page structure can keep things organized.

A research repository works when your team conducts research regularly across multiple products or domains, multiple people need to find and use past findings, you need to connect raw evidence (recordings, transcripts) to synthesized insights, and institutional knowledge loss is a real risk due to team turnover or organizational growth.

Many teams use a combination. For example, a repository like Dovetail serves as the system of record for all raw data and tagged insights, while a wiki is used to publish stakeholder-friendly summaries that link back to the repository for anyone who wants to see the evidence.

How to evaluate whether your current approach is working

If you are currently using a shared drive or wiki for research storage, ask yourself these questions:

Can a new team member find relevant past research without asking someone where it is? If the answer is no, your organizational system is failing.
When a stakeholder asks a question, how long does it take to check whether existing research already answers it? If the answer is "longer than a few minutes," insights are effectively lost.
Do you ever conduct a study only to discover halfway through that a similar study was done last year? Duplicated research is the clearest symptom of a storage problem.
Can you trace a product decision back to the research evidence that informed it? If evidence and decisions are disconnected, research loses its influence over time.
Are recordings and transcripts connected to the insights they produced? If raw data lives in one place and findings live in another, credibility and context erode.

If more than one of these questions reveals a problem, your team has likely outgrown its current storage approach.

Making the transition

Moving from a shared drive or wiki to a dedicated repository does not have to happen all at once. A practical migration path looks like this:

Start with new research. Run your next two or three studies entirely in the repository. This lets the team learn the tool and establish tagging conventions without the pressure of migrating historical data.
Migrate high-value past research. Identify the ten or twenty most-referenced studies and bring them into the repository. Tag and link them properly. This creates a critical mass of searchable content.
Retire the old system gradually. Once the repository is established as the primary home for insights, stop adding new research to the shared drive or wiki. Keep the old system accessible as an archive, but direct all new activity to the repository.
Invest in taxonomy. Spend time early on defining your tagging structure. Align tags to product areas, user segments, research questions, or whatever dimensions matter most for your organization's decision-making. This upfront work pays dividends for years.

Dovetail supports this kind of incremental adoption, allowing teams to import existing transcripts and recordings, build tagging taxonomies over time, and share insights with stakeholders through a browsable, searchable interface—without requiring a full migration before the tool becomes useful.

The real cost of lost insights

The discussion about shared drives vs. wikis vs. repositories is ultimately a discussion about whether past research continues to create value or quietly disappears.

Research is expensive. Every interview, usability session, and survey represents hours of planning, recruiting, facilitating, and analyzing. If the resulting insights are only useful for the project that produced them—because they are buried in a folder or lost in a wiki—that investment is largely wasted.

A research repository does not just store insights. It keeps them alive, findable, and connected to the decisions they can inform. For teams serious about building a lasting body of organizational knowledge, that is a meaningful difference.