A data reservoir is the working layer where raw records become useful: collected, cleaned, labeled, annotated, enhanced, joined, and turned into a signal someone can act on.

1. 01

   Collect

   Maps, charts, X posts, screenshots, files, and work notes enter as rough records, not finished knowledge.

2. 02

   Clean

   Broken rows, noisy text, stale paths, duplicates, and unsafe fields are removed before anything becomes public or automated.

3. 03

   Label

   Labeling gives a stable bucket: data type, domain, lane, topic, owner, or workflow stage.

4. 04

   Annotate

   Annotation adds context around the label: why it matters, confidence, privacy boundary, source shape, and whether the label should change with the target.

5. 05

   Enhance

   Enhancing adds summaries, calibrated categories, next actions, and the signal a downstream system should use.

6. 06

   Join

   Joining connects records across tools and time so the same work can become a timeline item, dashboard object, or automation input.

7. 07

   Signal

   The reservoir only pays off when a person or system can act on the signal without rereading the entire source pile.