Data validation assesses the degree to which incoming information conforms to defined requirements for its intended use. Addressing data anomalies at the intake stage preserves their context and prevents issues from becoming entrenched in subsequent processing.

Small differences in how fields are named or defined can impair downstream usability. Unresolved issues can affect how later information is interpreted. Values that do not meet the expected range are reviewed before they affect later processing.

When validation is incomplete, information that appears serviceable may still produce unreliable results. Differences in recorded values can thwart efforts to produce accurate results. It is critical to establish the proper inputs & desired outputs and have processes in place to ensure that is maintained through every step of the database.

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