Learn · Data Quality
Data quality checks
Every AI and analytics project lives or dies on data quality. A validation layer runs rules over each record before it flows downstream. Toggle the rules below and watch which values fail — and how the overall quality score responds.
Validation rules — tap to toggle:
Quality score
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Clean records
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Checks passed
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Failures
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■ a highlighted cell failed an active rule — hover it for the reason.
Why it matters
"Garbage in, garbage out" is the oldest rule in data — and it's exactly why models misbehave in production. A missing value, a malformed email, a duplicate ID or an out-of-range figure quietly corrupts analytics, trains biased models and breaks pipelines. Automated data-quality rules catch these at the door, produce an auditable score, and stop bad data before it spreads. It's unglamorous work that decides whether an AI project succeeds.
This is an illustrative sample. In production, rules like these run continuously in the pipeline (e.g. with Great Expectations, dbt tests or Spark jobs), with alerting and quarantine for records that fail.