Are your mapping tables reviewed and updated on a regular cadence?

Mapping tables should be reviewed monthly or more often to ensure AI queries work from accurate data. Tables updated only when issues are noticed are a common source of AI hallucinations.

Do the same field names mean the same thing across all your data models and pipelines?

Consistent naming conventions across all models are essential. Inconsistent field names across models cause AI systems to produce conflicting answers for the same metric.

How often does your team discover a data error through a downstream report rather than catching it upstream?

Errors should rarely or never be discovered downstream. Catching errors only in downstream reports means bad data is reaching AI systems and influencing decisions.

Is your transformation logic documented somewhere other than the original author's memory?

Transformation logic should be consistently documented in code comments, dbt model descriptions, or a knowledge base — not in any single person's head.

How long does it typically take from finding a data error to fixing it in production?

Data corrections should take less than a day. Multi-day correction cycles mean bad data persists long enough to become a trusted source for AI queries.

Do non-technical team members have a way to flag data errors without filing a ticket?

Non-technical users should be able to report data errors directly without filing a ticket or emailing an engineer. Ticket-based reporting slows corrections and keeps bad data in production longer.

Is your team using or planning to use AI to query or analyze your data?

If you are using or planning to use AI on your data, data hygiene becomes critical. AI systems answer confidently regardless of data quality, amplifying errors rather than flagging them.

When an AI answer is wrong, can you trace it back to which part of your pipeline produced the bad value?

You should be able to trace wrong AI answers back to specific fields, tables, or transformation logic. If you cannot trace the source, bad data will recur without a clear fix path.

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Is Your Data Stack Ready for AI?

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Most teams think their AI problem is a prompt problem. Usually it's a data problem. This assessment tells you which gaps in your stack are most likely to produce confident but wrong AI answers.

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