Secondary research

Secondary Research

Part of the discovery-phase skill pack · evidence group · reads discovery-context.md (run profile-builder first if missing).

Pull from published sources when primary research is constrained or unavailable. Standard tool for pre-sale discovery (no time/budget for primary), regulated domains (interview restrictions), and analogous-domain framing.

Step 1 — Read discovery context

Read discovery-context.md (sections 1. Client → Domain, 2. Product / Initiative, 6. Constraints) and problem-canvas.md if it exists.

If discovery-context.md is missing, ask the BA inline: "(a) client domain / sector; (b) any regulatory constraints (GDPR / HIPAA / SOC2 / none)?" — tag the output [ASSUMED DOMAIN]. Never block; recommend profile-builder for high-stakes work.

Step 2 — Decide research scope

Scope	Signal
Industry baseline	"We need to know what 'normal' looks like"
Best practice / state-of-the-art	"What have leading orgs done about this?"
Regulatory / compliance	"What's required vs forbidden?"
Analogous domain	"No data in our domain — can we borrow from <adjacent>?"
Sizing / TAM	"Is the opportunity even commercially worth it?"

A good secondary-research run usually picks 2-3 of these.

Step 3 — Delegate to web-research skills if available

If deep-research / exa-search / market-research are installed:

"Use deep-research for <scope> in <client domain>. Surface: <3-5 sub-questions>. Output cited."

Without those, fall back to: industry analyst reports (Gartner, Forrester, McKinsey, BCG public excerpts), regulatory body publications (FDA, GDPR-EU, FCA, etc.), academic search (Google Scholar, arXiv), vendor whitepapers (treat with skepticism), conference proceedings, public earnings call transcripts (surprisingly rich for sizing).

Step 4 — Capture findings with provenance

Per finding, capture:

• Claim — the specific assertion
• Source — URL, title, author, date
• Source quality — primary research / analyst report / vendor blog / forum / academic
• Recency — within 12 months / 1-3 years / older
• Implication for our hypothesis — supports / contradicts / orthogonal

Step 5 — Confidence and gap statement

End the doc with two short sections:

• What primary research would still be needed — even after secondary, what's a desk research can't tell us
• Confidence on each problem-canvas claim — high / medium / low, with reasoning

Step 6 — Analogous domain caveat

If using analogous domain (e.g., "no data on healthcare CRM, borrowing from financial-services CRM"): explicitly state where the analogy holds and where it breaks. Analogies smuggle wrong assumptions if not bounded.

Output

./discovery/secondary-research.md per ./template.md.

Append to _log.md: [secondary-research | YYYY-MM-DD] scopes: <list>; sources: <count>; supports: <N>; contradicts: <N>; orthogonal: <N>.

Anti-patterns

• Vendor blog as primary source. Treat as marketing, not data. Use only as "what they want you to think".
• Old reports. A 2020 SaaS benchmark is mostly irrelevant in 2026. Tag recency, prefer <18 months.
• Stacking weak sources to fake strength. Five vendor blogs ≠ one analyst report ≠ one peer-reviewed study. Don't conflate.
• No "what's missing" section. Without naming the gap, downstream synthesis assumes secondary is enough — usually wrong.