Glossary

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Glossary / Evaluation and implementation guide

Data Validation

Data validation is the enforcement of rules that determine whether a record is acceptable before it proceeds: required fields present, email syntactically valid, numbers within range, dates in an expected format.

In automation, validation usually sits at the entry point so bad data never triggers workflows, rather than being cleaned up after the fact.

A practical example

Example: a lead form accepts 'test@test' as an email. A validation rule at ingestion rejects or quarantines it, so no welcome sequence fires to an undeliverable address.

What to evaluate before investing

  • Ask whether validation rules run at ingestion, at each pipeline step, or only at the destination.
  • Check if invalid records are quarantined with a reason, or silently dropped.
  • Verify you can define custom rules per field, not just generic type checks.

Limitations and tradeoffs

Tradeoff: strict validation blocks bad data but also blocks edge cases; you need a review path for records that fail rules but are real.

Plan your next step with MeshLine

Connect this decision to your automation, organic marketing and customer lifecycle management. In a MeshLine demo, discuss your existing tools, the scope you need and how to measure the result.