Common Mistakes in Log Analysis

When analysts first encounter logs and unstructured files, a few common mistakes tend to appear.

The first mistake is treating logs like tables. Logs are event streams, not rows in a database.

Forcing them into a tabular mindset too early can hide important sequences and relationships.

The second mistake is over-filtering too early. Filtering out data before understanding

it can remove the very signals you’re trying to detect. Early filters often introduce bias.

A better approach is to explore broadly first, then narrow down once patterns emerge.

The third mistake is losing context. Individual log entries rarely tell the full story.

Context comes from surrounding events, timing, and correlations across files.