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Data Quality Toolkit

Validate, clean, compare, and score workflow data without runtime dependencies

Actions5

Generate Quality Report

AI-generated

Summary

Generate a data quality report by validating specified fields in each input item against user-defined rules and compute an overall quality score.

Inputs

  • Fields to Validate (required) — A list of field validation rules, each specifying the dot path field name, whether the field is required, if empty strings are allowed, and the expected data type (e.g., any, boolean, date, email, number, phone, string, URL).
  • Continue On Validation Failure — Whether to continue executing the workflow when validation errors occur (defaults to true).

Output shape

A list of items where each item's JSON includes the original data plus a 'dataQuality' property containing validation results and a quality score.

The output 'dataQuality' property includes 'valid' (boolean), 'score' (percentage of fields passing validation), 'checkedFields' (number of rules applied), 'errorCount' (number of validation errors), and an array of 'errors' detailing each failed validation with field name, error message, and expected type. If there are no fields to validate, the score defaults to 100 and the data is considered valid.

Examples

Example 1: Validate multiple fields in a customer record, checking presence, emptiness, and proper data types.

Specify field rules for fields such as 'customer.email' (required email), 'customer.phone' (optional phone number), and 'customer.signupDate' (required date). Enable or disable continuing on validation failure per workflow needs.

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