Module · The numbers your AI should not trust

The Data Quality Audit

AI learns whatever your data teaches it, including the lies. Some of your sources are gamed, some are stale, and most have no owner who would notice. This module finds which data is safe to feed a model, and which will teach it exactly the wrong thing.

Question 1 of 5 · Gamed sources are known

Do you know which of your data sources are gamed by the people who feed them?

Every metric someone is measured on eventually gets optimised, not always honestly. Sales pipelines, ticket close rates, utilisation: the numbers people's bonuses depend on are the numbers most likely to lie to a model.

Question 2 of 5 · Data is graded

Is your data graded for quality before anyone trusts it for a decision?

Not all data deserves the same confidence. A grade (verified, estimated, unverified) travels with the number and tells a model, or a human, how much weight it has earned.

Question 3 of 5 · Every dataset has an owner

Does each dataset that matters have a named owner accountable for its quality?

Ownership is the difference between an error that gets fixed and one that gets rediscovered every quarter. Not a custodian who stores the data: an owner who is called when it is wrong.

Question 4 of 5 · Know what to feed first

Do you know which of your data is trustworthy enough to feed an AI system first?

The instinct is to feed the model everything. The discipline is to feed it your cleanest, best-owned, ground-truthed data first, so it learns from your best evidence, not your worst.

Question 5 of 5 · Numbers trace to reality

Can you trace a key number back to where it came from and check it against reality?

Provenance is the difference between a number you can defend and one you merely repeat. If nobody can trace a figure to its source, nobody can tell when it quietly breaks.

For the statistics · one click each

Three questions for the public picture

These do not affect your score. They feed the anonymised, aggregated statistics; groups under 8 respondents are never shown.

Does your single most important dataset have a named owner?

Yes, one clear owner

Shared, unclear

IT holds it

No owner

We do not know

Have you ever discovered a key metric was being gamed?

Yes, and fixed it

Yes, still open

We suspect so

Never found one

We do not know

How much of your decision data would you stake a real decision on?

Most of it

About half

Very little

We are not sure

Your context

Used to calibrate the report. Company size and sector remain in the anonymized dataset; your email does not.

Company size Sector (optional)

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