Research Ops Playbook Template: Standard Intake, Publishing, Retention, and Deletion

A research ops playbook is a shared set of rules for how your team requests research, stores it, publishes it, and retires it. The goal is simple: anyone can find the latest, approved insights fast, and you reduce risk by keeping only what you should keep. This template gives you standard intake, naming, tagging, publishing, retention, and deletion practices, plus roles, SLAs, and a checklist you can reuse across projects.

Primary keyword: research ops playbook template.

Key takeaways

• Standardize intake so requests include goals, decisions, and constraints before work starts.
• Use one naming and tagging system so artifacts stay searchable across tools.
• Publish in layers (raw → working → final) with clear approval steps and access rules.
• Set retention and deletion rules up front to reduce clutter and privacy risk.
• Define roles and SLAs so stakeholders know what happens next and when.

What this Research Ops playbook covers (and how to use it)

This playbook template covers the end-to-end path of research artifacts, from intake to deletion. You can copy the sections into a doc, wiki page, or Notion space, then adjust the defaults to match your org’s tools and legal requirements.

Keep the playbook short, link to detailed SOPs, and review it on a set cadence (for example, quarterly). Treat it as “one way of working,” not a set of optional tips.

Core principles (keep these at the top of the playbook)

• Findable: Anyone with access can locate the latest approved output in under 2 minutes.
• Reusable: Artifacts have enough context to stand alone (who, what, when, why, how).
• Safe: Sensitive data has clear handling rules, and retention is intentional.
• Lightweight: The process fits the team’s pace, with “fast paths” for urgent needs.

Standard intake: request, triage, and kickoff

Intake is where research ops prevents rework. A good intake standard ensures every request states the decision it supports, the timeline, and how the output will be used.

Intake channels (pick 1–2, then close the rest)

• Primary: A single intake form (recommended).
• Secondary: A ticketing queue (Jira/Asana) that mirrors the form fields.

Route all “DM requests” back to the form so you can track scope, approvals, and SLAs.

Research request intake form (copy/paste template)

• Requester name + team:
• Project / product area:
• Decision to be made: What decision will this research inform?
• Research questions: 3–6 questions max.
• Target users / participants: Who are we learning from?
• What we already know: Links to prior research, analytics, support themes.
• Desired output: Readout, repository entry, video clips, transcript, dashboard, etc.
• Deadline: Date and why it is fixed.
• Priority: P0 (critical), P1, P2, P3 with criteria defined below.
• Constraints: Regions, languages, accessibility needs, privacy constraints.
• Stakeholders: Who must review and approve?
• Distribution: Who will need access to the artifacts?
• Data sensitivity: Low / Medium / High (define your levels).

Triage rules (simple decision criteria)

• Is it research? If it’s a question of “what happened,” route to analytics; if it’s “why,” keep in research.
• Is it already answered? Check repository first; share links and close the request if possible.
• Is the timeline realistic? Offer alternatives (lighter method, fewer segments, smaller sample).
• Do we have a clear decision? If not, schedule a 15–30 minute intake call.

SLAs for intake (template)

• Acknowledge request: within 1 business day.
• Triage decision (accept / decline / needs info): within 3 business days.
• Kickoff scheduled: within 5 business days of acceptance.

Track SLA exceptions in a simple log so you can fix capacity issues rather than argue about single cases.

Kickoff checklist (fast, consistent)

• Confirm the decision, not just the topic.
• Agree on success criteria for the study output (what “good” looks like).
• Confirm participant criteria and recruiting plan.
• Confirm privacy, consent, and storage constraints.
• Agree on deliverables and the publishing location.
• Set review points and final readout date.

Naming, tagging, and metadata: make research searchable

Most “we can’t find the research” problems come from inconsistent naming and missing metadata. Solve that with a single naming convention and a small set of required tags.

Standard naming convention (use everywhere)

Format: YYYY-MM-DD_ProductArea_Method_Topic_Region_or_Segment_V#

• Example: 2026-03-15_Checkout_Usability_Test_PromoCodes_US_NewUsers_V1
• Versioning: V1, V2… only when the content changes, not when the file moves.
• File types: Add suffixes like _Readout, _Transcript, _Notes, _Clips, _SurveyRaw.

Required metadata fields (minimum set)

• Owner: Researcher or DRI (directly responsible individual).
• Date range: When data was collected.
• Method: Interview, usability test, diary study, survey, concept test, etc.
• Participant profile: Who was included and who was not.
• Repository status: Draft / In review / Published / Archived.
• Sensitivity level: Low / Medium / High.
• Consent scope: Internal use only, training allowed, clip sharing allowed, etc.

Tagging taxonomy (keep it small)

• Product/area tags: Navigation, Search, Onboarding, Billing, Settings.
• User segment tags: New, Power, Admin, Mobile-only, Accessibility needs.
• Theme tags: Trust, Pricing clarity, Error recovery, Discoverability.
• Journey stage tags: Awareness, Setup, First value, Repeat use, Renewal.

Cap tags at 8–12 per artifact, and define “tag owners” who approve new tags so taxonomy does not explode.

Publishing workflow: from raw data to reusable insights

Publishing is not just “upload the deck.” A good workflow creates a reliable path from raw artifacts to a final summary that others can reuse with confidence.

Define three artifact layers

• Raw: recordings, raw survey exports, consent forms, unedited notes.
• Working: coded notes, affinity maps, preliminary themes, draft readouts.
• Published: final summary, key findings, recommendations, and links to supporting evidence.

Limit access to raw artifacts based on sensitivity, and prefer sharing published outputs widely.

Publishing package (what “done” includes)

• One-page summary: decision, scope, method, participants, key findings.
• Evidence links: timestamps, clips, quotes, screenshots, or tables.
• Recommendations: what to do next, plus confidence and dependencies.
• Limitations: what this research does not cover.
• Repository entry: named and tagged with required metadata.

Review and approval steps (template)

• Accuracy review: researcher + a peer reviewer check claims and evidence links.
• Stakeholder review: requester confirms decision coverage (not wordsmithing).
• Privacy review: confirm that published content matches consent and sensitivity rules.
• Publish: set status to Published, then announce in a single channel.

Publishing SLAs (template)

• Draft readout: within 5 business days after final session (adjust by method).
• Final published summary: within 10 business days after final session.
• Repository entry completed: same day as publishing.

If you cannot meet the SLA, publish a “minimum viable summary” first, then iterate.

Where to publish (keep the map simple)

• System of record: research repository (preferred) or a dedicated wiki database.
• Supporting files: secure drive folders that match the naming convention.
• Announcement: one shared channel plus a monthly digest.

Retention and deletion: reduce risk and clutter

Retention rules protect participants and your organization. They also keep your repository useful by removing outdated, redundant, or high-risk content.

Set retention based on sensitivity, consent scope, and the real value of keeping raw data. If you handle personal data, align your policy with applicable privacy rules and internal counsel.

Retention tiers (practical starting point)

• Tier 1 (Published summaries): keep longer because they are low risk and high reuse.
• Tier 2 (Working files): keep shorter; archive after the decision lands.
• Tier 3 (Raw recordings and identifiers): keep the shortest; restrict access heavily.

What to store vs. what to avoid

• Prefer storing: de-identified notes, synthesized findings, and short evidence clips that match consent.
• Avoid storing: unnecessary identifiers, duplicate exports, and “just in case” recordings.

Deletion workflow (make it auditable)

• Monthly scan: ops owner generates a list of items reaching retention limits.
• Owner review: artifact owner confirms whether an exception is needed.
• Delete or archive: follow the tier rules and document the action.
• Log: keep a simple deletion log (item, date, approver, reason).

For privacy concepts and definitions, many teams align their thinking with frameworks like the GDPR overview, especially around data minimization and storage limitation.

Exception handling (when you keep data longer)

• Allowed reasons: legal hold, ongoing product safety issue, active longitudinal study.
• Required approvals: research lead + privacy/security partner (as applicable).
• Time-box: every exception must have a new review date.

Roles and responsibilities (RACI you can paste)

Clear ownership prevents stalled publishing and messy storage. Use this RACI template and adapt titles to your org.

Role definitions

• Research Ops Lead (Ops DRI): owns the playbook, tooling standards, and audits.
• Researcher (Study DRI): owns study execution, artifacts, and publishing package.
• Requester / Product Partner: defines decision and reviews relevance.
• Repository Librarian (can be Ops): ensures naming, tagging, and metadata quality.
• Privacy/Security Partner: advises on access, retention, and sensitive data handling.

RACI table (condensed)

• Intake triage: R = Ops Lead, A = Research Lead, C = Requester, I = Team
• Study plan approval: R = Researcher, A = Research Lead, C = Requester, I = Ops
• Naming/tagging compliance: R = Librarian, A = Ops Lead, C = Researcher, I = Stakeholders
• Publishing approval: R = Researcher, A = Research Lead, C = Privacy/Security, I = Requester
• Retention/deletion execution: R = Ops Lead, A = Research Lead, C = Privacy/Security, I = Artifact owners

Operational SLAs (team-level)

• Repository QA: spot-check 10–20% of new entries each month.
• Taxonomy changes: review requests weekly; publish changes monthly.
• Playbook updates: quarterly review and change log.

Checklist: standardize research ops across projects

Use this checklist for every project, even small ones. It keeps operations consistent without adding heavy meetings.

Intake and setup

• Request submitted through the standard form.
• Decision, priority, and deadline confirmed.
• Existing research reviewed and linked.
• Sensitivity level set and access plan confirmed.
• Folder and repository entry created with correct name.

During the study

• Notes and files saved using the naming convention.
• Tags applied as themes emerge (do not wait until the end).
• Raw artifacts stored in the correct restricted location.
• Working artifacts stay in the project workspace, not in personal drives.

Publishing

• One-page summary completed with method and participant context.
• Evidence links added (quotes, timestamps, clips).
• Peer accuracy review completed.
• Privacy review completed for sensitive items.
• Repository status set to Published and announced.

After the decision

• Working files archived or deleted per policy.
• Raw recordings reviewed for retention limit and deleted when due.
• Final summary updated with outcomes (what shipped, what changed), if applicable.
• Study retro: 10 minutes to capture ops issues and fixes.

Common pitfalls (and how to avoid them)

Small ops gaps compound fast. These are the issues that most often break findability and trust in a research library.

• Too many intake paths: Close side channels and route everything to the form.
• Unclear owners: Assign a Study DRI and an Ops DRI for every project.
• Tag overload: Keep a controlled taxonomy and limit tags per artifact.
• Publishing only decks: Require a short summary and evidence links, even when you have slides.
• Keeping raw data forever: Set deletion defaults and log exceptions.
• Repository entries without context: Always include participant profile, method, and date range.

Common questions

How detailed should a research ops playbook be?

Keep the main playbook short and link out to SOPs. Your “standard” should fit on a few pages so people actually use it.

What if stakeholders need research results faster than the SLA?

Offer a fast path: publish a minimum summary first, then publish the full package later. Document the trade-offs (sample size, depth, or scope).

Do we need a separate repository tool?

Not always. You can start with a structured database in your wiki, as long as you enforce naming, required fields, and access rules.

How do we handle transcripts and recordings safely?

Store raw recordings in restricted folders, and publish de-identified excerpts when possible. If you use captions or transcripts for accessibility, the WCAG overview can help you align with accessibility expectations.

What tags should we standardize first?

Start with product area, method, user segment, and journey stage. Add theme tags once you see repeat patterns across studies.

How often should we delete or archive research?

Run a monthly retention sweep and a quarterly audit. If your org is small, quarterly may be enough at first.

How do we keep the playbook from becoming shelfware?

Build the checklist into your intake and publishing steps, and assign an ops owner to audit compliance. Update the playbook on a predictable cadence.

Where transcription and captions fit in research ops

Clear transcripts and time-coded notes make evidence easier to verify and reuse, especially when teams review research asynchronously. If you do interviews or usability sessions, consider standardizing how you request transcripts, store them, and link key quotes back to published findings.

• Set a standard transcript file name that matches the study naming convention.
• Store transcripts with the same sensitivity level as the recording they came from.
• Link quotes in the summary back to transcript timestamps or clip timestamps.

If you use AI to draft transcripts, a quick human review step can help catch speaker labels and key terms, especially for product names. You can also define a “transcript ready to publish” checklist before you share it widely.

For teams that want a mix of speed and structure, GoTranscript offers automated transcription as well as transcription proofreading services for polishing AI drafts.

When your process is ready, GoTranscript can support your research ops workflow with professional transcription services that fit into standardized intake, publishing, and retention practices.