Publications, trial registries, patents, conference abstracts, company announcements and regulatory documents contain enormous amounts of useful information. The problem is rarely access alone. The problem is turning fragmented evidence into a reliable answer.
Start with the decision
An intelligence project should begin with a decision question: Which targets are becoming crowded? Which assay platform is sufficiently validated? Where is a competitor’s programme most vulnerable? What evidence would change a purchasing decision?
Create a reproducible evidence map
Sources should be documented, dated and categorized. Search logic, inclusion rules and confidence judgments need to be visible so the conclusion can be updated rather than rebuilt.
Separate facts, interpretations and inferences
A trial registration is a fact. A company’s explanation of its strategy is an interpretation. A conclusion about why that strategy changed may be an inference. Keeping these layers separate protects the analysis from overconfidence.
Compare evidence on consistent fields
Programmes or products become comparable when the same questions are asked of each one: mechanism, stage, model, endpoint, validation, limitations, cost, adoption and unresolved risk.
Monitor change over time
Intelligence is most useful when it detects movement—new data, delayed milestones, leadership changes, partnerships, discontinued programmes or shifts in scientific language.
Deliver an action—not merely a report
The final output should explain what the evidence supports, what remains uncertain and what should be monitored or tested next. That is the difference between collecting scientific information and operating a scientific intelligence function.