Which practice helps ensure consistency between source data and the Clarity target during mapping?

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Multiple Choice

Which practice helps ensure consistency between source data and the Clarity target during mapping?

Explanation:
Ensuring consistency between source data and the Clarity target during mapping relies on validating data and reconciling results. Data quality checks formalize the validations you need: completeness, accuracy, consistency, validity, and timeliness, plus makes sure formats, units, and constraints align with what Clarity expects. Reconciliation goes further by comparing the transformed target data to the source to confirm every value is correctly mapped, transformed when needed, and that no data is lost or misrepresented. This combination catches mismatches, enforces business rules, and surfaces exceptions for correction before loading. Copying data without transformation risks incompatible formats, relying on manual observation is error-prone, and ignoring data quality lets issues propagate—none of these ensure the same level of guaranteed consistency as checks plus reconciliation.

Ensuring consistency between source data and the Clarity target during mapping relies on validating data and reconciling results. Data quality checks formalize the validations you need: completeness, accuracy, consistency, validity, and timeliness, plus makes sure formats, units, and constraints align with what Clarity expects. Reconciliation goes further by comparing the transformed target data to the source to confirm every value is correctly mapped, transformed when needed, and that no data is lost or misrepresented. This combination catches mismatches, enforces business rules, and surfaces exceptions for correction before loading. Copying data without transformation risks incompatible formats, relying on manual observation is error-prone, and ignoring data quality lets issues propagate—none of these ensure the same level of guaranteed consistency as checks plus reconciliation.

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