Postmortem Pattern Mining

Problem

No reviewer can hold thousands of long, inconsistently-written postmortems in working memory at once, and the recurring pattern only becomes visible when the whole corpus is compared. Reading them serially is too slow to keep current, sampling a handful misses the long tail, and a single pass over the concatenated text overflows any context window and blurs distinct incidents into mush. The organisation is forced to choose between never extracting the cross-incident trend or paying for a manual read that is stale before it finishes.

Solution

Treat the archive as a map-fold problem. A per-document map stage sends each postmortem to a model that summarises it and emits a normalised record — cause category, affected component, trigger, mitigation, severity — against a fixed taxonomy. A classify stage snaps free-text causes onto that taxonomy so distinct documents become comparable rows. An analyse stage clusters the rows and ranks recurring causes by frequency, recency, and severity. A final aggregate stage drafts a one-page report of the dominant trends and patterns. Because a single hallucinated or miscategorised record poisons the count, a human reviewer samples the per-document records before the aggregate stage runs, and the report cites the underlying records so any claimed trend traces back to specific postmortems.

When to use

• A large corpus of written incident postmortems exists and the cross-document trend matters more than any single document.
• Reading the whole corpus by hand is too slow to stay current, so the cross-incident signal goes unextracted.
• A fixed cause taxonomy can be agreed on so free-text postmortems can be normalised into comparable records.