GIGO (Garbage In, Garbage Out)
Definition
Garbage in, garbage out: every report, forecast and AI recommendation is exactly as reliable as the data feeding it. A mis-mapped channel, a dumping-ground segment code or a stale rate plan doesn’t just distort one number — it flows through every layer built on top.
What it tells you
Bad data is more dangerous than missing data, because it looks like a full-value answer. A forecast trained on bookings filed under the wrong segment will be confidently wrong — and the better your tooling, the further a single upstream error travels. GIGO is the reason data governance is a revenue discipline, not an IT chore.
How to track it
A monthly data-hygiene audit: segment and rate-code mapping spot-checks, duplicate-profile counts, share of bookings landing in “other”/unmapped buckets, and a change log for anything that touches how reservations are recorded. Watch trends, not single readings — quality decays quietly.
Where it fits
The foundation under every analytics and AI lesson: forecast accuracy, segment analysis and machine recommendations all inherit the quality of the source data. The free Academy covers it in depth: RM and data quality — governance, GIGO.