Data Hygiene & Normalization

Normalize names and flag gaps so comparisons remain useful.

Qualitynormalizationnaminggapsduplicates
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Overview

Consistent naming and de-duplicated records make comparisons possible. Hygiene isn’t glamorous, but it’s what turns charts into decisions.

Normalize where it helps (e.g., per-post averages), but keep the original values available for auditing.

Key Ideas

  • Standardize account and metric names.
  • Flag missing data and refresh failures early.
  • Avoid destructive transforms; store raw + derived.
  • Document known gaps and how to interpret them.

Simple Flow

  1. Create a simple naming convention.
  2. Automate duplicate/missing checks.
  3. Track raw and normalized values side-by-side.
  4. Log issues and resolutions for transparency.
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FAQs

  • Why do numbers sometimes jump? Late API updates, deduping, or timezone shifts can change totals. Document your rules and show refresh times.
  • Do we need normalization for small accounts? Lightweight averages help, but avoid overprocessing tiny samples.
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