design-a-desk-research-plan

name	design-a-desk-research-plan
category	data
description	Plan bounded secondary research around a concrete decision, evidence map, and stopping rule. Use when a team needs reliable answers from existing sources on a deadline.

design-a-desk-research-plan

Turn “research this” into a scoped evidence job with decision relevance, source priorities, and explicit gaps.

When to use

• Use for market, policy, product, operational, technical, or competitive questions based mainly on existing material.
• Do not substitute desk research for user research, measurement, legal advice, or experimentation when direct evidence is required.

Procedure

1. Define the decision, audience, deadline, deliverable, and cost of a wrong answer.
2. Convert the request into a primary question and a small set of non-overlapping subquestions.
3. Set scope by geography, period, segment, definitions, and exclusions.
4. Build an evidence map: needed claim, preferred primary source, acceptable secondary source, freshness requirement, and validation method.
5. Prioritize sources by directness and authority, including official records, datasets, filings, standards, research, and credible reporting.
6. Write search strategies and a source log format before collection.
7. Assign owners, review checkpoints, and timeboxes.
8. Define stopping rules using coverage, source saturation, unresolved high-risk gaps, and diminishing decision value.
9. Specify the output structure: findings, evidence, counterevidence, confidence, gaps, implications, and recommended next research.

Failure plan

• If the decision changes, re-scope before continuing.
• If evidence depends on volatile facts, schedule a final refresh.
• If the available sources cannot answer the question, state that and propose direct research.
• Do not present target markets, search phrases, or management beliefs as verified demand.

Done

• Questions, scope, evidence needs, source priorities, owners, and stopping rules are explicit
• The plan distinguishes facts to verify from assumptions and decision preferences
• The deliverable can expose gaps instead of rewarding volume